Update logbook: Reproduction: Distributed Direct Preference Optimization
Browse files- .serve.log +0 -0
- README.md +17 -5
- bucket-icon.svg +5 -0
- index.html +82 -17
- logbook.css +2142 -0
- logbook.js +0 -0
- logbook.json +101 -0
- pages/claim-1-feddpo-partial-participation-bound/page.md +835 -0
- pages/claim-2-full-participation-corollary/page.md +21 -0
- pages/claim-3-staleness-penalty/page.md +19 -0
- pages/claim-4-feddpo-lower-bound/page.md +19 -0
- pages/claim-5-decdpo-spectral-rate/page.md +48 -0
- pages/claim-6-shp-numerical-results/page.md +2080 -0
- pages/conclusion/page.md +18 -0
- pages/executive-summary/page.md +0 -0
- pages/index.md +14 -0
- trackio-logo-light.png +0 -0
- trackio-logo.png +0 -0
- trackio-wordmark-dark.png +0 -0
- workspace.json +29 -0
.serve.log
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README.md
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---
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title:
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---
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---
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title: "Reproduction: Distributed Direct Preference Optimization"
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emoji: 🎯
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colorFrom: yellow
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colorTo: red
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sdk: static
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pinned: false
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tags:
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- trackio
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- trackio-logbook
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- open-experiment
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- trackio
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- open-reproductions
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- icml2026
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- icml2026-repro
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- paper-ljNZyrAlaa
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- arxiv:2605.20696
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---
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# Reproduction: Distributed Direct Preference Optimization
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An open experiment logbook, published with [Trackio](https://github.com/gradio-app/trackio).
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bucket-icon.svg
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index.html
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<!doctype html>
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</html>
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>Reproduction: Distributed Direct Preference Optimization</title>
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<link rel="stylesheet" href="./logbook.css" />
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</head>
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<body>
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<div id="app">
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<aside id="sidebar">
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<div id="book-head">
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<img id="book-wordmark" src="./trackio-wordmark-dark.png" alt="" />
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<div id="book-title" class="sr-only">Logbook</div>
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</div>
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<nav id="tree"></nav>
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<div id="sidebar-foot" hidden>
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<button id="connect-btn" type="button">
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<span class="ico">ⓘ</span> Collaborate with your agent
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</button>
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</div>
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</aside>
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<main id="content">
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<nav id="view-tabs" aria-label="Logbook views">
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<a data-view="code" href="#/view/code/index">
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<svg viewBox="0 0 24 24" aria-hidden="true">
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<path d="m18 16 4-4-4-4" />
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<path d="m6 8-4 4 4 4" />
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<path d="m14.5 4-5 16" />
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</svg>
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<span>Logbook</span>
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</a>
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<a data-view="trace" href="#/view/trace">
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<svg viewBox="0 0 24 24" aria-hidden="true">
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<path d="M8 5h13" />
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<path d="M13 12h8" />
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<path d="M13 19h8" />
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<path d="M3 10a2 2 0 0 0 2 2h3" />
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<path d="M3 5v12a2 2 0 0 0 2 2h3" />
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</svg>
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<span>Traces</span>
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</a>
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<a data-view="workspace" href="#/view/workspace">
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<svg viewBox="0 0 24 24" aria-hidden="true">
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<path d="M20 20a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.9a2 2 0 0 1-1.69-.9L9.6 3.9A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13a2 2 0 0 0 2 2Z" />
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</svg>
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<span>Workspace</span>
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</a>
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</nav>
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<header id="logbook-header">
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<h1 id="logbook-title"></h1>
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<div id="logbook-cli"></div>
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</header>
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<div id="page"></div>
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</main>
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</div>
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<div id="modal" hidden>
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<div class="modal-backdrop"></div>
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<div class="modal-card" role="dialog" aria-modal="true">
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<div class="modal-head">
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<div class="modal-title">
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<img class="modal-logo" src="./trackio-logo.png" alt="" />
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Collaborate with your agent
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</div>
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<div class="modal-actions">
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<button id="copy-agent" class="btn">Copy for agent</button>
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<button id="modal-close" class="btn icon" aria-label="Close">×</button>
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</div>
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</div>
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<div class="modal-body">
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<p class="modal-intro">
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Point your coding agent at this logbook. It reads a compact,
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token-efficient version — and if you've given it write access to this
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Space, it can add findings that sync back automatically.
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</p>
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<ol id="connect-steps"></ol>
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</div>
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</div>
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</div>
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<script src="./logbook.js"></script>
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</body>
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</html>
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logbook.css
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|
| 1 |
+
:root {
|
| 2 |
+
--bg: #ffffff;
|
| 3 |
+
--paper: #ffffff;
|
| 4 |
+
--panel: #ffffff;
|
| 5 |
+
--ink: #1f2937;
|
| 6 |
+
--muted: #6b7280;
|
| 7 |
+
--line: #e5e7eb;
|
| 8 |
+
--accent: #f97316;
|
| 9 |
+
--accent-strong: #ea580c;
|
| 10 |
+
--accent-soft: #fff7ed;
|
| 11 |
+
--accent-line: rgba(249, 115, 22, 0.16);
|
| 12 |
+
--grid-line: rgba(31, 41, 55, 0.02);
|
| 13 |
+
--code-bg: #f3f4f6;
|
| 14 |
+
--radius: 12px;
|
| 15 |
+
--sidebar-width: 280px;
|
| 16 |
+
--content-gutter: 40px;
|
| 17 |
+
--serif: ui-serif, "Iowan Old Style", "Palatino Linotype", Georgia, serif;
|
| 18 |
+
--sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial,
|
| 19 |
+
sans-serif;
|
| 20 |
+
--mono: "SFMono-Regular", "Cascadia Mono", "JetBrains Mono", Menlo, Consolas,
|
| 21 |
+
ui-monospace, monospace;
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
* {
|
| 25 |
+
box-sizing: border-box;
|
| 26 |
+
}
|
| 27 |
+
|
| 28 |
+
html,
|
| 29 |
+
body {
|
| 30 |
+
margin: 0;
|
| 31 |
+
padding: 0;
|
| 32 |
+
}
|
| 33 |
+
|
| 34 |
+
html {
|
| 35 |
+
scroll-behavior: smooth;
|
| 36 |
+
scrollbar-gutter: stable;
|
| 37 |
+
}
|
| 38 |
+
|
| 39 |
+
body {
|
| 40 |
+
background: var(--bg);
|
| 41 |
+
color: var(--ink);
|
| 42 |
+
font-family: var(--sans);
|
| 43 |
+
font-size: 13px;
|
| 44 |
+
line-height: 1.65;
|
| 45 |
+
-webkit-font-smoothing: antialiased;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
#app {
|
| 49 |
+
display: flex;
|
| 50 |
+
min-height: 100vh;
|
| 51 |
+
}
|
| 52 |
+
|
| 53 |
+
body[data-view="trace"] #sidebar-foot,
|
| 54 |
+
body[data-view="workspace"] #sidebar-foot {
|
| 55 |
+
display: none;
|
| 56 |
+
}
|
| 57 |
+
|
| 58 |
+
/* ---- sidebar (composition-book cover) ---- */
|
| 59 |
+
#sidebar {
|
| 60 |
+
width: var(--sidebar-width);
|
| 61 |
+
flex: 0 0 var(--sidebar-width);
|
| 62 |
+
background: #17181c;
|
| 63 |
+
color: #e7e7ea;
|
| 64 |
+
position: sticky;
|
| 65 |
+
top: 0;
|
| 66 |
+
height: 100vh;
|
| 67 |
+
overflow-y: auto;
|
| 68 |
+
padding: 22px 16px;
|
| 69 |
+
display: flex;
|
| 70 |
+
flex-direction: column;
|
| 71 |
+
}
|
| 72 |
+
|
| 73 |
+
#book-head {
|
| 74 |
+
display: flex;
|
| 75 |
+
align-items: center;
|
| 76 |
+
gap: 10px;
|
| 77 |
+
padding: 8px;
|
| 78 |
+
margin-bottom: 12px;
|
| 79 |
+
border-radius: 10px;
|
| 80 |
+
cursor: pointer;
|
| 81 |
+
transition: background 0.12s;
|
| 82 |
+
}
|
| 83 |
+
#book-head:hover {
|
| 84 |
+
background: rgba(255, 255, 255, 0.05);
|
| 85 |
+
}
|
| 86 |
+
#book-wordmark {
|
| 87 |
+
width: 154px;
|
| 88 |
+
height: auto;
|
| 89 |
+
object-fit: contain;
|
| 90 |
+
}
|
| 91 |
+
.sr-only {
|
| 92 |
+
position: absolute;
|
| 93 |
+
width: 1px;
|
| 94 |
+
height: 1px;
|
| 95 |
+
padding: 0;
|
| 96 |
+
margin: -1px;
|
| 97 |
+
overflow: hidden;
|
| 98 |
+
clip: rect(0, 0, 0, 0);
|
| 99 |
+
white-space: nowrap;
|
| 100 |
+
border: 0;
|
| 101 |
+
}
|
| 102 |
+
|
| 103 |
+
#tree {
|
| 104 |
+
flex: 1;
|
| 105 |
+
padding-top: 8px;
|
| 106 |
+
}
|
| 107 |
+
|
| 108 |
+
#tree .tree-label {
|
| 109 |
+
padding: 6px 10px 8px;
|
| 110 |
+
color: #777a83;
|
| 111 |
+
font-size: 10px;
|
| 112 |
+
font-weight: 700;
|
| 113 |
+
letter-spacing: 0.12em;
|
| 114 |
+
text-transform: uppercase;
|
| 115 |
+
}
|
| 116 |
+
|
| 117 |
+
#tree a {
|
| 118 |
+
display: block;
|
| 119 |
+
padding: 6px 10px;
|
| 120 |
+
border-radius: 8px;
|
| 121 |
+
color: #c3c4cb;
|
| 122 |
+
text-decoration: none;
|
| 123 |
+
font-size: 14px;
|
| 124 |
+
transition: background 0.12s, color 0.12s;
|
| 125 |
+
overflow: hidden;
|
| 126 |
+
text-overflow: ellipsis;
|
| 127 |
+
white-space: nowrap;
|
| 128 |
+
}
|
| 129 |
+
|
| 130 |
+
#tree a:hover {
|
| 131 |
+
background: rgba(255, 255, 255, 0.06);
|
| 132 |
+
color: #ffffff;
|
| 133 |
+
}
|
| 134 |
+
|
| 135 |
+
#tree a.active {
|
| 136 |
+
background: rgba(249, 115, 22, 0.16);
|
| 137 |
+
color: #fdba74;
|
| 138 |
+
font-weight: 600;
|
| 139 |
+
}
|
| 140 |
+
|
| 141 |
+
#tree a .tree-mark {
|
| 142 |
+
color: #6b6d76;
|
| 143 |
+
}
|
| 144 |
+
|
| 145 |
+
#tree a:hover .tree-mark,
|
| 146 |
+
#tree a.active .tree-mark {
|
| 147 |
+
color: inherit;
|
| 148 |
+
opacity: 0.6;
|
| 149 |
+
}
|
| 150 |
+
|
| 151 |
+
#tree .depth-1 {
|
| 152 |
+
padding-left: 22px;
|
| 153 |
+
}
|
| 154 |
+
#tree .depth-2 {
|
| 155 |
+
padding-left: 34px;
|
| 156 |
+
}
|
| 157 |
+
#tree .depth-3 {
|
| 158 |
+
padding-left: 46px;
|
| 159 |
+
}
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
/* ---- content ---- */
|
| 163 |
+
#content {
|
| 164 |
+
flex: 1;
|
| 165 |
+
min-width: 0;
|
| 166 |
+
padding: 24px
|
| 167 |
+
clamp(
|
| 168 |
+
var(--content-gutter),
|
| 169 |
+
calc(100vw - 960px),
|
| 170 |
+
calc(var(--sidebar-width) + var(--content-gutter))
|
| 171 |
+
)
|
| 172 |
+
120px var(--content-gutter);
|
| 173 |
+
background-color: var(--paper);
|
| 174 |
+
background-image:
|
| 175 |
+
linear-gradient(var(--grid-line) 1px, transparent 1px),
|
| 176 |
+
linear-gradient(90deg, var(--grid-line) 1px, transparent 1px);
|
| 177 |
+
background-size: 26px 26px;
|
| 178 |
+
background-position: center top;
|
| 179 |
+
}
|
| 180 |
+
|
| 181 |
+
#logbook-header {
|
| 182 |
+
width: 100%;
|
| 183 |
+
max-width: 1080px;
|
| 184 |
+
margin: 0 auto 20px;
|
| 185 |
+
}
|
| 186 |
+
#logbook-title {
|
| 187 |
+
font-family: var(--serif);
|
| 188 |
+
font-size: 34px;
|
| 189 |
+
line-height: 1.15;
|
| 190 |
+
letter-spacing: -0.02em;
|
| 191 |
+
margin: 0 0 10px;
|
| 192 |
+
overflow-wrap: anywhere;
|
| 193 |
+
}
|
| 194 |
+
#logbook-cli {
|
| 195 |
+
display: grid;
|
| 196 |
+
gap: 7px;
|
| 197 |
+
}
|
| 198 |
+
|
| 199 |
+
#page {
|
| 200 |
+
width: 100%;
|
| 201 |
+
min-width: 0;
|
| 202 |
+
max-width: 1080px;
|
| 203 |
+
margin: 0 auto;
|
| 204 |
+
}
|
| 205 |
+
|
| 206 |
+
.page-section {
|
| 207 |
+
scroll-margin-top: 40px;
|
| 208 |
+
padding: 0 0 35px;
|
| 209 |
+
margin: 0 0 32px;
|
| 210 |
+
}
|
| 211 |
+
|
| 212 |
+
.page-section:last-child {
|
| 213 |
+
margin-bottom: 0;
|
| 214 |
+
}
|
| 215 |
+
|
| 216 |
+
.page-layout {
|
| 217 |
+
display: block;
|
| 218 |
+
}
|
| 219 |
+
|
| 220 |
+
.page-body {
|
| 221 |
+
min-width: 0;
|
| 222 |
+
}
|
| 223 |
+
|
| 224 |
+
.resource-anchor {
|
| 225 |
+
display: block;
|
| 226 |
+
height: 0;
|
| 227 |
+
overflow: hidden;
|
| 228 |
+
}
|
| 229 |
+
|
| 230 |
+
/* ---- pinned notes ---- */
|
| 231 |
+
.pinned-notes {
|
| 232 |
+
margin: 30px 0 32px;
|
| 233 |
+
}
|
| 234 |
+
.pinned-notes-list .cell {
|
| 235 |
+
margin: 0;
|
| 236 |
+
}
|
| 237 |
+
.pinned-notes-list .cell-title {
|
| 238 |
+
display: flex;
|
| 239 |
+
align-items: center;
|
| 240 |
+
gap: 7px;
|
| 241 |
+
}
|
| 242 |
+
.pin-ico {
|
| 243 |
+
flex: 0 0 auto;
|
| 244 |
+
width: 14px;
|
| 245 |
+
height: 14px;
|
| 246 |
+
fill: var(--accent);
|
| 247 |
+
stroke: none;
|
| 248 |
+
}
|
| 249 |
+
.pinned-notes-list .cell + .cell {
|
| 250 |
+
margin-top: 12px;
|
| 251 |
+
}
|
| 252 |
+
.book-intro.has-pinned-notes {
|
| 253 |
+
border-bottom: none;
|
| 254 |
+
padding-bottom: 22px;
|
| 255 |
+
margin-bottom: 30px;
|
| 256 |
+
}
|
| 257 |
+
.book-intro.book-intro-tight {
|
| 258 |
+
border-bottom: none;
|
| 259 |
+
padding-bottom: 4px;
|
| 260 |
+
margin-bottom: 20px;
|
| 261 |
+
}
|
| 262 |
+
|
| 263 |
+
#page h1 {
|
| 264 |
+
font-family: var(--serif);
|
| 265 |
+
font-size: 34px;
|
| 266 |
+
line-height: 1.15;
|
| 267 |
+
letter-spacing: -0.02em;
|
| 268 |
+
margin: 0 0 8px;
|
| 269 |
+
overflow-wrap: anywhere;
|
| 270 |
+
}
|
| 271 |
+
|
| 272 |
+
#page .page-section:not(.book-intro) h1 {
|
| 273 |
+
font-size: 26px;
|
| 274 |
+
}
|
| 275 |
+
|
| 276 |
+
#page h2 {
|
| 277 |
+
font-family: var(--serif);
|
| 278 |
+
font-size: 24px;
|
| 279 |
+
margin: 36px 0 10px;
|
| 280 |
+
}
|
| 281 |
+
|
| 282 |
+
#page h3 {
|
| 283 |
+
font-size: 17px;
|
| 284 |
+
font-weight: 700;
|
| 285 |
+
margin: 26px 0 2px;
|
| 286 |
+
letter-spacing: -0.01em;
|
| 287 |
+
}
|
| 288 |
+
|
| 289 |
+
#page h3::before {
|
| 290 |
+
content: "";
|
| 291 |
+
display: inline-block;
|
| 292 |
+
width: 7px;
|
| 293 |
+
height: 7px;
|
| 294 |
+
border-radius: 2px;
|
| 295 |
+
background: var(--accent);
|
| 296 |
+
margin-right: 10px;
|
| 297 |
+
vertical-align: middle;
|
| 298 |
+
transform: translateY(-1px);
|
| 299 |
+
}
|
| 300 |
+
|
| 301 |
+
#page p {
|
| 302 |
+
margin: 10px 0;
|
| 303 |
+
}
|
| 304 |
+
|
| 305 |
+
#page blockquote {
|
| 306 |
+
margin: 14px 0;
|
| 307 |
+
padding: 2px 16px;
|
| 308 |
+
border-left: 3px solid #fdba74;
|
| 309 |
+
color: var(--muted);
|
| 310 |
+
}
|
| 311 |
+
|
| 312 |
+
#page hr {
|
| 313 |
+
display: none;
|
| 314 |
+
}
|
| 315 |
+
|
| 316 |
+
#page code {
|
| 317 |
+
font-family: var(--mono);
|
| 318 |
+
font-size: 0.86em;
|
| 319 |
+
background: var(--code-bg);
|
| 320 |
+
padding: 2px 6px;
|
| 321 |
+
border-radius: 6px;
|
| 322 |
+
}
|
| 323 |
+
|
| 324 |
+
#page pre {
|
| 325 |
+
max-width: 100%;
|
| 326 |
+
background: var(--code-bg);
|
| 327 |
+
border: 1px solid var(--line);
|
| 328 |
+
border-radius: var(--radius);
|
| 329 |
+
padding: 14px 16px;
|
| 330 |
+
overflow-x: auto;
|
| 331 |
+
}
|
| 332 |
+
#page pre code {
|
| 333 |
+
background: none;
|
| 334 |
+
padding: 0;
|
| 335 |
+
font-size: 11.5px;
|
| 336 |
+
}
|
| 337 |
+
|
| 338 |
+
/* ---- code blocks + collapsible accordion ---- */
|
| 339 |
+
#page pre.hl {
|
| 340 |
+
background: #17181c;
|
| 341 |
+
border: none;
|
| 342 |
+
color: #e7e7ea;
|
| 343 |
+
font-size: 13px;
|
| 344 |
+
line-height: 1.58;
|
| 345 |
+
}
|
| 346 |
+
#page pre.hl code {
|
| 347 |
+
color: inherit;
|
| 348 |
+
font-family: var(--mono);
|
| 349 |
+
}
|
| 350 |
+
.code-accordion {
|
| 351 |
+
border: 1px solid rgba(249, 115, 22, 0.2);
|
| 352 |
+
border-radius: 8px;
|
| 353 |
+
overflow: hidden;
|
| 354 |
+
margin: 12px 0;
|
| 355 |
+
background: #17181c;
|
| 356 |
+
}
|
| 357 |
+
.code-accordion summary {
|
| 358 |
+
list-style: none;
|
| 359 |
+
cursor: pointer;
|
| 360 |
+
display: flex;
|
| 361 |
+
align-items: center;
|
| 362 |
+
gap: 9px;
|
| 363 |
+
padding: 9px 12px;
|
| 364 |
+
font-family: var(--mono);
|
| 365 |
+
font-size: 11.5px;
|
| 366 |
+
font-weight: 700;
|
| 367 |
+
color: #e7e7ea;
|
| 368 |
+
background: #1e2027;
|
| 369 |
+
user-select: none;
|
| 370 |
+
overflow-wrap: anywhere;
|
| 371 |
+
}
|
| 372 |
+
.code-accordion summary::-webkit-details-marker {
|
| 373 |
+
display: none;
|
| 374 |
+
}
|
| 375 |
+
.code-accordion summary::after {
|
| 376 |
+
content: "▸";
|
| 377 |
+
margin-left: auto;
|
| 378 |
+
color: var(--accent);
|
| 379 |
+
transition: transform 0.12s;
|
| 380 |
+
transform: rotate(180deg);
|
| 381 |
+
}
|
| 382 |
+
.code-accordion[open] summary::after {
|
| 383 |
+
transform: rotate(90deg);
|
| 384 |
+
}
|
| 385 |
+
.code-accordion .code-ico {
|
| 386 |
+
color: var(--accent);
|
| 387 |
+
font-weight: 700;
|
| 388 |
+
}
|
| 389 |
+
.code-accordion pre.hl {
|
| 390 |
+
margin: 0;
|
| 391 |
+
border-radius: 0;
|
| 392 |
+
border: none;
|
| 393 |
+
border-top: 1px solid rgba(249, 115, 22, 0.16);
|
| 394 |
+
}
|
| 395 |
+
.tok-comment {
|
| 396 |
+
color: #7a7d87;
|
| 397 |
+
font-style: italic;
|
| 398 |
+
}
|
| 399 |
+
.tok-string {
|
| 400 |
+
color: #a5d6a7;
|
| 401 |
+
}
|
| 402 |
+
.tok-keyword {
|
| 403 |
+
color: #fdba74;
|
| 404 |
+
}
|
| 405 |
+
.tok-number {
|
| 406 |
+
color: #7fd0e0;
|
| 407 |
+
}
|
| 408 |
+
|
| 409 |
+
#page a {
|
| 410 |
+
color: var(--accent);
|
| 411 |
+
}
|
| 412 |
+
|
| 413 |
+
#page ul {
|
| 414 |
+
padding-left: 20px;
|
| 415 |
+
}
|
| 416 |
+
|
| 417 |
+
.ts {
|
| 418 |
+
font-family: var(--mono);
|
| 419 |
+
font-size: 12px;
|
| 420 |
+
color: var(--muted);
|
| 421 |
+
background: none;
|
| 422 |
+
padding: 0;
|
| 423 |
+
}
|
| 424 |
+
|
| 425 |
+
/* ---- notebook-style cells ---- */
|
| 426 |
+
.cell {
|
| 427 |
+
max-width: 100%;
|
| 428 |
+
margin: 0 0 32px;
|
| 429 |
+
background: none;
|
| 430 |
+
border: none;
|
| 431 |
+
border-radius: 0;
|
| 432 |
+
box-shadow: none;
|
| 433 |
+
overflow: visible;
|
| 434 |
+
}
|
| 435 |
+
.cell-head {
|
| 436 |
+
display: flex;
|
| 437 |
+
justify-content: space-between;
|
| 438 |
+
gap: 16px;
|
| 439 |
+
align-items: baseline;
|
| 440 |
+
padding: 0 0 5px;
|
| 441 |
+
background: none;
|
| 442 |
+
border-bottom: none;
|
| 443 |
+
}
|
| 444 |
+
.cell-head.no-title {
|
| 445 |
+
justify-content: flex-end;
|
| 446 |
+
padding: 0 0 3px;
|
| 447 |
+
}
|
| 448 |
+
.cell-title {
|
| 449 |
+
flex: 1;
|
| 450 |
+
min-width: 0;
|
| 451 |
+
font-size: 13px;
|
| 452 |
+
font-weight: 650;
|
| 453 |
+
color: var(--ink);
|
| 454 |
+
line-height: 1.35;
|
| 455 |
+
overflow-wrap: anywhere;
|
| 456 |
+
}
|
| 457 |
+
.cell-meta {
|
| 458 |
+
flex: 0 0 auto;
|
| 459 |
+
display: flex;
|
| 460 |
+
align-items: center;
|
| 461 |
+
gap: 10px;
|
| 462 |
+
font-family: var(--sans);
|
| 463 |
+
font-size: 13px;
|
| 464 |
+
color: var(--muted);
|
| 465 |
+
}
|
| 466 |
+
.cell-open {
|
| 467 |
+
flex: 0 0 auto;
|
| 468 |
+
font-family: var(--mono);
|
| 469 |
+
font-size: 12px;
|
| 470 |
+
color: var(--accent);
|
| 471 |
+
text-decoration: none;
|
| 472 |
+
}
|
| 473 |
+
.cell-open:hover {
|
| 474 |
+
color: var(--accent-strong);
|
| 475 |
+
}
|
| 476 |
+
.cell-body {
|
| 477 |
+
min-width: 0;
|
| 478 |
+
padding: 0;
|
| 479 |
+
}
|
| 480 |
+
.cell.dashboard .cell-body {
|
| 481 |
+
padding: 0;
|
| 482 |
+
}
|
| 483 |
+
#page .cell-body h1,
|
| 484 |
+
#page .cell-body h2 {
|
| 485 |
+
font-family: var(--sans);
|
| 486 |
+
font-size: 17px;
|
| 487 |
+
font-weight: 700;
|
| 488 |
+
letter-spacing: -0.01em;
|
| 489 |
+
line-height: 1.35;
|
| 490 |
+
margin: 22px 0 6px;
|
| 491 |
+
}
|
| 492 |
+
#page .cell-body > :first-child {
|
| 493 |
+
margin-top: 0;
|
| 494 |
+
}
|
| 495 |
+
#page .cell-body > :last-child {
|
| 496 |
+
margin-bottom: 0;
|
| 497 |
+
}
|
| 498 |
+
.figure-fit {
|
| 499 |
+
position: relative;
|
| 500 |
+
overflow: hidden;
|
| 501 |
+
min-height: 160px;
|
| 502 |
+
border: 1px solid var(--line);
|
| 503 |
+
border-radius: 8px;
|
| 504 |
+
background: #fff;
|
| 505 |
+
}
|
| 506 |
+
.figure-fit[hidden] {
|
| 507 |
+
display: none;
|
| 508 |
+
}
|
| 509 |
+
.figure-fit:fullscreen,
|
| 510 |
+
.figure-fit:-webkit-full-screen {
|
| 511 |
+
width: 100%;
|
| 512 |
+
height: 100%;
|
| 513 |
+
border: none;
|
| 514 |
+
border-radius: 0;
|
| 515 |
+
}
|
| 516 |
+
.figure-frame {
|
| 517 |
+
display: block;
|
| 518 |
+
width: 100%;
|
| 519 |
+
min-height: 160px;
|
| 520 |
+
border: none;
|
| 521 |
+
background: #fff;
|
| 522 |
+
}
|
| 523 |
+
.figure-frame[hidden],
|
| 524 |
+
.figure-raw[hidden] {
|
| 525 |
+
display: none;
|
| 526 |
+
}
|
| 527 |
+
.fig-switch {
|
| 528 |
+
position: relative;
|
| 529 |
+
display: inline-flex;
|
| 530 |
+
flex: 0 0 auto;
|
| 531 |
+
border: 1px solid var(--line);
|
| 532 |
+
border-radius: 999px;
|
| 533 |
+
background: var(--code-bg);
|
| 534 |
+
padding: 2px;
|
| 535 |
+
}
|
| 536 |
+
.fig-switch button {
|
| 537 |
+
position: relative;
|
| 538 |
+
z-index: 1;
|
| 539 |
+
flex: 1;
|
| 540 |
+
min-width: 62px;
|
| 541 |
+
border: none;
|
| 542 |
+
background: none;
|
| 543 |
+
font-family: var(--sans);
|
| 544 |
+
font-size: 12px;
|
| 545 |
+
font-weight: 600;
|
| 546 |
+
color: var(--muted);
|
| 547 |
+
padding: 3px 12px;
|
| 548 |
+
border-radius: 999px;
|
| 549 |
+
cursor: pointer;
|
| 550 |
+
transition: color 0.15s;
|
| 551 |
+
}
|
| 552 |
+
.fig-switch button.active {
|
| 553 |
+
color: var(--accent-strong);
|
| 554 |
+
}
|
| 555 |
+
.fig-switch-thumb {
|
| 556 |
+
position: absolute;
|
| 557 |
+
top: 2px;
|
| 558 |
+
bottom: 2px;
|
| 559 |
+
left: 2px;
|
| 560 |
+
width: calc(50% - 2px);
|
| 561 |
+
border-radius: 999px;
|
| 562 |
+
background: var(--panel);
|
| 563 |
+
border: 1px solid rgba(249, 115, 22, 0.35);
|
| 564 |
+
box-shadow: 0 1px 4px rgba(31, 41, 55, 0.08);
|
| 565 |
+
transition: transform 0.18s ease;
|
| 566 |
+
}
|
| 567 |
+
.fig-switch.raw .fig-switch-thumb {
|
| 568 |
+
transform: translateX(100%);
|
| 569 |
+
}
|
| 570 |
+
#page .figure-raw pre {
|
| 571 |
+
margin: 0;
|
| 572 |
+
max-height: 420px;
|
| 573 |
+
overflow: auto;
|
| 574 |
+
font-family: var(--mono);
|
| 575 |
+
font-size: 13px;
|
| 576 |
+
line-height: 1.55;
|
| 577 |
+
background: var(--code-bg);
|
| 578 |
+
border: 1px solid var(--line);
|
| 579 |
+
border-radius: 8px;
|
| 580 |
+
padding: 12px 14px;
|
| 581 |
+
}
|
| 582 |
+
/* ---- figure fullscreen ---- */
|
| 583 |
+
.cell-fullscreen {
|
| 584 |
+
position: relative;
|
| 585 |
+
display: inline-flex;
|
| 586 |
+
flex: 0 0 auto;
|
| 587 |
+
}
|
| 588 |
+
.cell-fullscreen-btn {
|
| 589 |
+
display: inline-flex;
|
| 590 |
+
align-items: center;
|
| 591 |
+
justify-content: center;
|
| 592 |
+
width: 26px;
|
| 593 |
+
height: 26px;
|
| 594 |
+
padding: 0;
|
| 595 |
+
border: 1px solid var(--line);
|
| 596 |
+
border-radius: 999px;
|
| 597 |
+
background: var(--code-bg);
|
| 598 |
+
color: var(--muted);
|
| 599 |
+
cursor: pointer;
|
| 600 |
+
transition: color 0.15s, border-color 0.15s, background 0.15s;
|
| 601 |
+
}
|
| 602 |
+
.cell-fullscreen-btn:hover {
|
| 603 |
+
color: var(--accent-strong);
|
| 604 |
+
border-color: rgba(249, 115, 22, 0.35);
|
| 605 |
+
background: var(--accent-soft);
|
| 606 |
+
}
|
| 607 |
+
.cell-fullscreen-btn svg {
|
| 608 |
+
width: 14px;
|
| 609 |
+
height: 14px;
|
| 610 |
+
}
|
| 611 |
+
/* ---- copyable snippets ---- */
|
| 612 |
+
.snippet {
|
| 613 |
+
position: relative;
|
| 614 |
+
}
|
| 615 |
+
.copy-snippet {
|
| 616 |
+
position: absolute;
|
| 617 |
+
top: 7px;
|
| 618 |
+
right: 8px;
|
| 619 |
+
width: 24px;
|
| 620 |
+
height: 24px;
|
| 621 |
+
border: none;
|
| 622 |
+
border-radius: 6px;
|
| 623 |
+
background: rgba(255, 255, 255, 0.08);
|
| 624 |
+
color: #9a9da8;
|
| 625 |
+
font-size: 12px;
|
| 626 |
+
line-height: 1;
|
| 627 |
+
cursor: pointer;
|
| 628 |
+
opacity: 0;
|
| 629 |
+
transition: opacity 0.12s, color 0.12s, background 0.12s;
|
| 630 |
+
}
|
| 631 |
+
.snippet:hover .copy-snippet,
|
| 632 |
+
.jp-out:hover .copy-snippet,
|
| 633 |
+
.figure-raw:hover .copy-snippet,
|
| 634 |
+
.code-accordion summary:hover .copy-snippet {
|
| 635 |
+
opacity: 1;
|
| 636 |
+
}
|
| 637 |
+
.copy-snippet:hover {
|
| 638 |
+
color: #ffffff;
|
| 639 |
+
background: rgba(255, 255, 255, 0.16);
|
| 640 |
+
}
|
| 641 |
+
.copy-snippet.copied {
|
| 642 |
+
color: #52d08a;
|
| 643 |
+
opacity: 1;
|
| 644 |
+
}
|
| 645 |
+
.code-accordion .code-name {
|
| 646 |
+
user-select: text;
|
| 647 |
+
cursor: text;
|
| 648 |
+
}
|
| 649 |
+
.jp-out,
|
| 650 |
+
.figure-raw {
|
| 651 |
+
position: relative;
|
| 652 |
+
}
|
| 653 |
+
.jp-out .copy-snippet,
|
| 654 |
+
.figure-raw .copy-snippet {
|
| 655 |
+
background: var(--code-bg);
|
| 656 |
+
color: var(--muted);
|
| 657 |
+
border: 1px solid var(--line);
|
| 658 |
+
}
|
| 659 |
+
.jp-out .copy-snippet:hover,
|
| 660 |
+
.figure-raw .copy-snippet:hover {
|
| 661 |
+
color: var(--accent-strong);
|
| 662 |
+
background: var(--panel);
|
| 663 |
+
}
|
| 664 |
+
|
| 665 |
+
/* ---- jupyter-style code cells ---- */
|
| 666 |
+
.jp {
|
| 667 |
+
border: 1px solid var(--line);
|
| 668 |
+
border-radius: 10px;
|
| 669 |
+
overflow: hidden;
|
| 670 |
+
margin: 0;
|
| 671 |
+
background: var(--panel);
|
| 672 |
+
}
|
| 673 |
+
.jp-cmd {
|
| 674 |
+
display: flex;
|
| 675 |
+
align-items: baseline;
|
| 676 |
+
gap: 9px;
|
| 677 |
+
position: relative;
|
| 678 |
+
padding: 10px 16px 10px 0;
|
| 679 |
+
font-family: var(--mono);
|
| 680 |
+
font-size: 12px;
|
| 681 |
+
color: #8b8e98;
|
| 682 |
+
}
|
| 683 |
+
.jp-cmd-prompt {
|
| 684 |
+
color: var(--accent);
|
| 685 |
+
font-weight: 700;
|
| 686 |
+
}
|
| 687 |
+
#page .jp-cmd code {
|
| 688 |
+
min-width: 0;
|
| 689 |
+
color: #b6b9c2;
|
| 690 |
+
font-family: var(--mono);
|
| 691 |
+
font-size: 12px;
|
| 692 |
+
background: none;
|
| 693 |
+
padding: 0;
|
| 694 |
+
border-radius: 0;
|
| 695 |
+
overflow-wrap: anywhere;
|
| 696 |
+
}
|
| 697 |
+
.jp-cmd:hover .copy-snippet {
|
| 698 |
+
opacity: 1;
|
| 699 |
+
}
|
| 700 |
+
.jp-in-body .jp-cmd + .code-accordion,
|
| 701 |
+
.jp-in-body .jp-cmd + .snippet {
|
| 702 |
+
border-top: 1px solid rgba(255, 255, 255, 0.09);
|
| 703 |
+
}
|
| 704 |
+
.jp-gutter {
|
| 705 |
+
flex: 0 0 46px;
|
| 706 |
+
padding: 13px 0 0 13px;
|
| 707 |
+
font-family: var(--mono);
|
| 708 |
+
font-size: 10.5px;
|
| 709 |
+
letter-spacing: 0.07em;
|
| 710 |
+
text-transform: uppercase;
|
| 711 |
+
font-weight: 600;
|
| 712 |
+
user-select: none;
|
| 713 |
+
}
|
| 714 |
+
.jp-in {
|
| 715 |
+
display: flex;
|
| 716 |
+
background: #17181c;
|
| 717 |
+
}
|
| 718 |
+
.jp-in .jp-gutter {
|
| 719 |
+
color: #6f727d;
|
| 720 |
+
}
|
| 721 |
+
.jp-in-body {
|
| 722 |
+
flex: 1;
|
| 723 |
+
min-width: 0;
|
| 724 |
+
}
|
| 725 |
+
#page .jp-in-body pre.hl {
|
| 726 |
+
margin: 0;
|
| 727 |
+
border: none;
|
| 728 |
+
border-radius: 0;
|
| 729 |
+
background: none;
|
| 730 |
+
padding: 12px 16px 12px 0;
|
| 731 |
+
overflow-y: auto;
|
| 732 |
+
max-height: 26em;
|
| 733 |
+
}
|
| 734 |
+
.jp-in-body .code-accordion {
|
| 735 |
+
margin: 0;
|
| 736 |
+
border: none;
|
| 737 |
+
border-top: 1px solid rgba(255, 255, 255, 0.09);
|
| 738 |
+
border-radius: 0;
|
| 739 |
+
background: none;
|
| 740 |
+
}
|
| 741 |
+
.jp-in-body .code-accordion summary {
|
| 742 |
+
background: none;
|
| 743 |
+
padding: 9px 16px 9px 0;
|
| 744 |
+
}
|
| 745 |
+
.jp-in-body .code-accordion pre.hl {
|
| 746 |
+
border-top: 1px solid rgba(255, 255, 255, 0.09);
|
| 747 |
+
}
|
| 748 |
+
.jp-meta {
|
| 749 |
+
padding: 5px 14px;
|
| 750 |
+
font-family: var(--mono);
|
| 751 |
+
font-size: 11.5px;
|
| 752 |
+
color: var(--muted);
|
| 753 |
+
background: #fbfbfc;
|
| 754 |
+
border-top: 1px solid var(--line);
|
| 755 |
+
}
|
| 756 |
+
.jp-out {
|
| 757 |
+
display: flex;
|
| 758 |
+
border-top: 1px solid var(--line);
|
| 759 |
+
background: var(--panel);
|
| 760 |
+
}
|
| 761 |
+
.jp-out .jp-gutter {
|
| 762 |
+
color: var(--accent-strong);
|
| 763 |
+
}
|
| 764 |
+
.jp-out-body {
|
| 765 |
+
flex: 1;
|
| 766 |
+
min-width: 0;
|
| 767 |
+
}
|
| 768 |
+
#page .jp-out-pre {
|
| 769 |
+
min-width: 0;
|
| 770 |
+
margin: 0;
|
| 771 |
+
border: none;
|
| 772 |
+
border-radius: 0;
|
| 773 |
+
background: none;
|
| 774 |
+
color: var(--ink);
|
| 775 |
+
font-family: var(--mono);
|
| 776 |
+
font-size: 13px;
|
| 777 |
+
line-height: 1.55;
|
| 778 |
+
padding: 12px 16px 12px 0;
|
| 779 |
+
white-space: pre;
|
| 780 |
+
overflow-x: auto;
|
| 781 |
+
overflow-y: auto;
|
| 782 |
+
max-height: 26em;
|
| 783 |
+
}
|
| 784 |
+
.jp-artifacts {
|
| 785 |
+
display: flex;
|
| 786 |
+
flex-direction: column;
|
| 787 |
+
}
|
| 788 |
+
.jp-out-body .jp-out-pre + .jp-artifacts {
|
| 789 |
+
border-top: 1px solid var(--line);
|
| 790 |
+
}
|
| 791 |
+
.out-artifact {
|
| 792 |
+
display: flex;
|
| 793 |
+
align-items: baseline;
|
| 794 |
+
gap: 8px;
|
| 795 |
+
padding: 9px 16px 9px 0;
|
| 796 |
+
text-decoration: none;
|
| 797 |
+
color: inherit;
|
| 798 |
+
}
|
| 799 |
+
.out-artifact + .out-artifact {
|
| 800 |
+
border-top: 1px solid var(--line);
|
| 801 |
+
}
|
| 802 |
+
a.out-artifact:hover .out-artifact-name {
|
| 803 |
+
color: var(--accent-strong);
|
| 804 |
+
}
|
| 805 |
+
.out-artifact-ico {
|
| 806 |
+
flex: 0 0 auto;
|
| 807 |
+
font-size: 13px;
|
| 808 |
+
}
|
| 809 |
+
.out-artifact-name {
|
| 810 |
+
font-family: var(--mono);
|
| 811 |
+
font-size: 12.5px;
|
| 812 |
+
font-weight: 600;
|
| 813 |
+
color: var(--ink);
|
| 814 |
+
overflow: hidden;
|
| 815 |
+
text-overflow: ellipsis;
|
| 816 |
+
white-space: nowrap;
|
| 817 |
+
}
|
| 818 |
+
.out-artifact-meta {
|
| 819 |
+
flex: 0 0 auto;
|
| 820 |
+
margin-left: auto;
|
| 821 |
+
padding-left: 12px;
|
| 822 |
+
font-size: 12px;
|
| 823 |
+
color: var(--muted);
|
| 824 |
+
white-space: nowrap;
|
| 825 |
+
}
|
| 826 |
+
.out-artifact-state.open {
|
| 827 |
+
color: var(--accent);
|
| 828 |
+
font-weight: 600;
|
| 829 |
+
}
|
| 830 |
+
.trackio-embed {
|
| 831 |
+
border: 1px solid var(--line);
|
| 832 |
+
border-radius: var(--radius);
|
| 833 |
+
overflow: hidden;
|
| 834 |
+
background: var(--panel);
|
| 835 |
+
}
|
| 836 |
+
.trackio-cell-meta {
|
| 837 |
+
display: flex;
|
| 838 |
+
gap: 6px;
|
| 839 |
+
flex-wrap: wrap;
|
| 840 |
+
justify-content: flex-end;
|
| 841 |
+
}
|
| 842 |
+
|
| 843 |
+
/* ---- unfurl cards ---- */
|
| 844 |
+
.unfurl {
|
| 845 |
+
display: block;
|
| 846 |
+
border: 1px solid var(--line);
|
| 847 |
+
border-radius: var(--radius);
|
| 848 |
+
background: var(--panel);
|
| 849 |
+
margin: 12px 0;
|
| 850 |
+
overflow: hidden;
|
| 851 |
+
text-decoration: none;
|
| 852 |
+
color: inherit;
|
| 853 |
+
transition: border-color 0.14s, box-shadow 0.14s;
|
| 854 |
+
}
|
| 855 |
+
.unfurl:hover {
|
| 856 |
+
border-color: #cfcbe6;
|
| 857 |
+
box-shadow: 0 4px 18px rgba(30, 20, 80, 0.06);
|
| 858 |
+
}
|
| 859 |
+
|
| 860 |
+
.unfurl-body {
|
| 861 |
+
padding: 13px 16px;
|
| 862 |
+
display: flex;
|
| 863 |
+
gap: 12px;
|
| 864 |
+
align-items: flex-start;
|
| 865 |
+
}
|
| 866 |
+
|
| 867 |
+
.unfurl-ico {
|
| 868 |
+
font-size: 20px;
|
| 869 |
+
line-height: 1.3;
|
| 870 |
+
flex: 0 0 auto;
|
| 871 |
+
}
|
| 872 |
+
|
| 873 |
+
.unfurl-main {
|
| 874 |
+
min-width: 0;
|
| 875 |
+
flex: 1;
|
| 876 |
+
}
|
| 877 |
+
|
| 878 |
+
.unfurl-kind {
|
| 879 |
+
font-family: var(--mono);
|
| 880 |
+
font-size: 10.5px;
|
| 881 |
+
text-transform: uppercase;
|
| 882 |
+
letter-spacing: 0.08em;
|
| 883 |
+
color: var(--accent);
|
| 884 |
+
font-weight: 600;
|
| 885 |
+
}
|
| 886 |
+
|
| 887 |
+
.unfurl-title {
|
| 888 |
+
font-weight: 650;
|
| 889 |
+
font-size: 15px;
|
| 890 |
+
margin: 1px 0 2px;
|
| 891 |
+
white-space: nowrap;
|
| 892 |
+
overflow: hidden;
|
| 893 |
+
text-overflow: ellipsis;
|
| 894 |
+
}
|
| 895 |
+
|
| 896 |
+
.unfurl-desc {
|
| 897 |
+
color: var(--muted);
|
| 898 |
+
font-size: 13.5px;
|
| 899 |
+
line-height: 1.45;
|
| 900 |
+
}
|
| 901 |
+
|
| 902 |
+
.unfurl-meta {
|
| 903 |
+
margin-top: 6px;
|
| 904 |
+
display: flex;
|
| 905 |
+
flex-wrap: wrap;
|
| 906 |
+
gap: 6px;
|
| 907 |
+
}
|
| 908 |
+
|
| 909 |
+
.chip {
|
| 910 |
+
font-size: 11.5px;
|
| 911 |
+
background: var(--code-bg);
|
| 912 |
+
border-radius: 999px;
|
| 913 |
+
padding: 2px 9px;
|
| 914 |
+
color: var(--muted);
|
| 915 |
+
font-family: var(--mono);
|
| 916 |
+
}
|
| 917 |
+
|
| 918 |
+
.unfurl-raw {
|
| 919 |
+
font-family: var(--mono);
|
| 920 |
+
font-size: 11px;
|
| 921 |
+
color: var(--muted);
|
| 922 |
+
border-top: 1px solid var(--line);
|
| 923 |
+
padding: 7px 16px;
|
| 924 |
+
white-space: nowrap;
|
| 925 |
+
overflow: hidden;
|
| 926 |
+
text-overflow: ellipsis;
|
| 927 |
+
}
|
| 928 |
+
|
| 929 |
+
.unfurl.embed {
|
| 930 |
+
padding: 0;
|
| 931 |
+
overflow: hidden;
|
| 932 |
+
}
|
| 933 |
+
.embed-head {
|
| 934 |
+
display: flex;
|
| 935 |
+
align-items: center;
|
| 936 |
+
gap: 10px;
|
| 937 |
+
padding: 10px 14px;
|
| 938 |
+
border-bottom: 1px solid var(--line);
|
| 939 |
+
}
|
| 940 |
+
.embed-head .unfurl-kind {
|
| 941 |
+
flex: 0 0 auto;
|
| 942 |
+
}
|
| 943 |
+
.embed-title {
|
| 944 |
+
flex: 1;
|
| 945 |
+
min-width: 0;
|
| 946 |
+
font-weight: 650;
|
| 947 |
+
font-size: 14px;
|
| 948 |
+
color: var(--ink);
|
| 949 |
+
text-decoration: none;
|
| 950 |
+
white-space: nowrap;
|
| 951 |
+
overflow: hidden;
|
| 952 |
+
text-overflow: ellipsis;
|
| 953 |
+
}
|
| 954 |
+
.embed-title:hover {
|
| 955 |
+
color: var(--accent);
|
| 956 |
+
}
|
| 957 |
+
.embed-open {
|
| 958 |
+
flex: 0 0 auto;
|
| 959 |
+
font-family: var(--mono);
|
| 960 |
+
font-size: 12px;
|
| 961 |
+
color: var(--accent);
|
| 962 |
+
text-decoration: none;
|
| 963 |
+
}
|
| 964 |
+
.embed-frame {
|
| 965 |
+
display: block;
|
| 966 |
+
width: 100%;
|
| 967 |
+
height: 560px;
|
| 968 |
+
border: 0;
|
| 969 |
+
background: var(--code-bg);
|
| 970 |
+
}
|
| 971 |
+
|
| 972 |
+
.dashboard-shell {
|
| 973 |
+
display: block;
|
| 974 |
+
}
|
| 975 |
+
.dashboard-shell .dashboard-frame {
|
| 976 |
+
display: block;
|
| 977 |
+
width: 100%;
|
| 978 |
+
height: 900px;
|
| 979 |
+
border: 0;
|
| 980 |
+
background: var(--code-bg);
|
| 981 |
+
}
|
| 982 |
+
|
| 983 |
+
.unfurl.image {
|
| 984 |
+
padding: 0;
|
| 985 |
+
}
|
| 986 |
+
.unfurl.image img {
|
| 987 |
+
display: block;
|
| 988 |
+
width: 100%;
|
| 989 |
+
height: auto;
|
| 990 |
+
max-height: 460px;
|
| 991 |
+
object-fit: contain;
|
| 992 |
+
background: var(--code-bg);
|
| 993 |
+
}
|
| 994 |
+
|
| 995 |
+
.artifact-chip {
|
| 996 |
+
border: 1px solid var(--line);
|
| 997 |
+
background: var(--panel);
|
| 998 |
+
border-radius: var(--radius);
|
| 999 |
+
padding: 10px 14px;
|
| 1000 |
+
margin: 8px 0;
|
| 1001 |
+
font-size: 14px;
|
| 1002 |
+
}
|
| 1003 |
+
.cell.dashboard .artifact-chip {
|
| 1004 |
+
margin: 14px 18px 18px;
|
| 1005 |
+
}
|
| 1006 |
+
.artifact-chip code {
|
| 1007 |
+
color: var(--accent);
|
| 1008 |
+
}
|
| 1009 |
+
|
| 1010 |
+
/* ---- task board ---- */
|
| 1011 |
+
.board-wrap {
|
| 1012 |
+
overflow-x: auto;
|
| 1013 |
+
border: 1px solid var(--line);
|
| 1014 |
+
border-radius: var(--radius);
|
| 1015 |
+
margin: 12px 0 20px;
|
| 1016 |
+
background: var(--panel);
|
| 1017 |
+
}
|
| 1018 |
+
table.board {
|
| 1019 |
+
border-collapse: collapse;
|
| 1020 |
+
width: 100%;
|
| 1021 |
+
font-size: 14px;
|
| 1022 |
+
}
|
| 1023 |
+
table.board th,
|
| 1024 |
+
table.board td {
|
| 1025 |
+
text-align: left;
|
| 1026 |
+
padding: 9px 14px;
|
| 1027 |
+
border-bottom: 1px solid var(--line);
|
| 1028 |
+
vertical-align: top;
|
| 1029 |
+
}
|
| 1030 |
+
table.board thead th {
|
| 1031 |
+
background: var(--accent-soft);
|
| 1032 |
+
font-size: 12px;
|
| 1033 |
+
text-transform: uppercase;
|
| 1034 |
+
letter-spacing: 0.05em;
|
| 1035 |
+
color: #9a4a12;
|
| 1036 |
+
font-weight: 600;
|
| 1037 |
+
border-bottom: 1px solid var(--line);
|
| 1038 |
+
}
|
| 1039 |
+
table.board tbody tr:last-child td {
|
| 1040 |
+
border-bottom: none;
|
| 1041 |
+
}
|
| 1042 |
+
table.board .col-check {
|
| 1043 |
+
text-align: center;
|
| 1044 |
+
width: 92px;
|
| 1045 |
+
white-space: nowrap;
|
| 1046 |
+
}
|
| 1047 |
+
table.board tr.section-row td {
|
| 1048 |
+
background: var(--accent-soft);
|
| 1049 |
+
text-align: center;
|
| 1050 |
+
font-weight: 700;
|
| 1051 |
+
font-size: 13px;
|
| 1052 |
+
color: var(--accent-strong);
|
| 1053 |
+
padding: 7px 14px;
|
| 1054 |
+
letter-spacing: 0.02em;
|
| 1055 |
+
}
|
| 1056 |
+
.box {
|
| 1057 |
+
display: inline-flex;
|
| 1058 |
+
align-items: center;
|
| 1059 |
+
justify-content: center;
|
| 1060 |
+
width: 18px;
|
| 1061 |
+
height: 18px;
|
| 1062 |
+
border: 1.5px solid #cfcbe0;
|
| 1063 |
+
border-radius: 5px;
|
| 1064 |
+
font-size: 12px;
|
| 1065 |
+
color: #fff;
|
| 1066 |
+
line-height: 1;
|
| 1067 |
+
}
|
| 1068 |
+
.box.on {
|
| 1069 |
+
background: var(--accent);
|
| 1070 |
+
border-color: var(--accent);
|
| 1071 |
+
}
|
| 1072 |
+
.who-chip {
|
| 1073 |
+
display: inline-block;
|
| 1074 |
+
padding: 3px 12px;
|
| 1075 |
+
border-radius: 999px;
|
| 1076 |
+
font-size: 12.5px;
|
| 1077 |
+
font-weight: 600;
|
| 1078 |
+
white-space: nowrap;
|
| 1079 |
+
}
|
| 1080 |
+
.who-chip.muted {
|
| 1081 |
+
background: var(--code-bg);
|
| 1082 |
+
color: var(--muted);
|
| 1083 |
+
font-weight: 500;
|
| 1084 |
+
}
|
| 1085 |
+
|
| 1086 |
+
/* ---- status badges + clickable rows ---- */
|
| 1087 |
+
table.board .col-status {
|
| 1088 |
+
width: 130px;
|
| 1089 |
+
white-space: nowrap;
|
| 1090 |
+
}
|
| 1091 |
+
.badge {
|
| 1092 |
+
display: inline-block;
|
| 1093 |
+
padding: 3px 11px;
|
| 1094 |
+
border-radius: 999px;
|
| 1095 |
+
font-size: 12px;
|
| 1096 |
+
font-weight: 600;
|
| 1097 |
+
letter-spacing: 0.01em;
|
| 1098 |
+
}
|
| 1099 |
+
.badge.gray {
|
| 1100 |
+
background: var(--code-bg);
|
| 1101 |
+
color: var(--muted);
|
| 1102 |
+
}
|
| 1103 |
+
.badge.amber {
|
| 1104 |
+
background: var(--accent-soft);
|
| 1105 |
+
color: #b45309;
|
| 1106 |
+
}
|
| 1107 |
+
.badge.green {
|
| 1108 |
+
background: #e6f7ee;
|
| 1109 |
+
color: #1a8a55;
|
| 1110 |
+
}
|
| 1111 |
+
.badge.red {
|
| 1112 |
+
background: #fde8ec;
|
| 1113 |
+
color: #c62a4b;
|
| 1114 |
+
}
|
| 1115 |
+
table.board tr.linked-row {
|
| 1116 |
+
cursor: pointer;
|
| 1117 |
+
}
|
| 1118 |
+
table.board tr.linked-row:hover td {
|
| 1119 |
+
background: var(--accent-soft);
|
| 1120 |
+
}
|
| 1121 |
+
table.board tr.linked-row a {
|
| 1122 |
+
color: var(--ink);
|
| 1123 |
+
font-weight: 600;
|
| 1124 |
+
text-decoration: none;
|
| 1125 |
+
}
|
| 1126 |
+
table.board tr.linked-row:hover a {
|
| 1127 |
+
color: var(--accent-strong);
|
| 1128 |
+
}
|
| 1129 |
+
|
| 1130 |
+
/* ---- agent read hint ---- */
|
| 1131 |
+
.agent-hint {
|
| 1132 |
+
display: flex;
|
| 1133 |
+
align-items: center;
|
| 1134 |
+
flex-wrap: wrap;
|
| 1135 |
+
gap: 8px;
|
| 1136 |
+
margin: 0;
|
| 1137 |
+
font-size: 12.5px;
|
| 1138 |
+
color: var(--muted);
|
| 1139 |
+
}
|
| 1140 |
+
.agent-hint code {
|
| 1141 |
+
flex: 1 1 18rem;
|
| 1142 |
+
min-width: 0;
|
| 1143 |
+
background: var(--code-bg);
|
| 1144 |
+
padding: 2px 9px;
|
| 1145 |
+
border-radius: 6px;
|
| 1146 |
+
font-family: var(--mono);
|
| 1147 |
+
font-size: 12px;
|
| 1148 |
+
font-weight: 500;
|
| 1149 |
+
color: var(--ink);
|
| 1150 |
+
overflow: hidden;
|
| 1151 |
+
text-overflow: ellipsis;
|
| 1152 |
+
white-space: nowrap;
|
| 1153 |
+
}
|
| 1154 |
+
.agent-hint .copy {
|
| 1155 |
+
flex: 0 0 auto;
|
| 1156 |
+
background: none;
|
| 1157 |
+
color: var(--muted);
|
| 1158 |
+
border: 1px solid var(--line);
|
| 1159 |
+
border-radius: 6px;
|
| 1160 |
+
width: 22px;
|
| 1161 |
+
height: 22px;
|
| 1162 |
+
font-size: 11px;
|
| 1163 |
+
line-height: 1;
|
| 1164 |
+
cursor: pointer;
|
| 1165 |
+
transition: color 0.12s, border-color 0.12s;
|
| 1166 |
+
}
|
| 1167 |
+
.agent-hint .copy:hover {
|
| 1168 |
+
color: var(--accent-strong);
|
| 1169 |
+
border-color: var(--accent);
|
| 1170 |
+
}
|
| 1171 |
+
.agent-hint .copy.copied {
|
| 1172 |
+
color: #1a8a55;
|
| 1173 |
+
border-color: #1a8a55;
|
| 1174 |
+
}
|
| 1175 |
+
.agent-hint-note {
|
| 1176 |
+
margin-left: auto;
|
| 1177 |
+
font-size: 12px;
|
| 1178 |
+
color: var(--muted);
|
| 1179 |
+
}
|
| 1180 |
+
.hub-destination {
|
| 1181 |
+
display: flex;
|
| 1182 |
+
align-items: center;
|
| 1183 |
+
flex-wrap: wrap;
|
| 1184 |
+
gap: 8px;
|
| 1185 |
+
color: var(--muted);
|
| 1186 |
+
font-size: 12.5px;
|
| 1187 |
+
}
|
| 1188 |
+
.hub-destination a {
|
| 1189 |
+
display: inline-flex;
|
| 1190 |
+
align-items: center;
|
| 1191 |
+
gap: 6px;
|
| 1192 |
+
max-width: 100%;
|
| 1193 |
+
padding: 3px 9px;
|
| 1194 |
+
border: 1px solid var(--accent-line);
|
| 1195 |
+
border-radius: 999px;
|
| 1196 |
+
background: var(--accent-soft);
|
| 1197 |
+
color: var(--accent-strong);
|
| 1198 |
+
font-family: var(--mono);
|
| 1199 |
+
font-size: 12px;
|
| 1200 |
+
font-weight: 650;
|
| 1201 |
+
line-height: 1.5;
|
| 1202 |
+
text-decoration: none;
|
| 1203 |
+
overflow-wrap: anywhere;
|
| 1204 |
+
transition: border-color 0.12s, background 0.12s, color 0.12s;
|
| 1205 |
+
}
|
| 1206 |
+
.hub-destination a:hover {
|
| 1207 |
+
border-color: var(--accent);
|
| 1208 |
+
background: #ffedd5;
|
| 1209 |
+
color: #c2410c;
|
| 1210 |
+
}
|
| 1211 |
+
.hub-destination svg {
|
| 1212 |
+
width: 13px;
|
| 1213 |
+
height: 13px;
|
| 1214 |
+
flex: 0 0 auto;
|
| 1215 |
+
fill: none;
|
| 1216 |
+
stroke: currentColor;
|
| 1217 |
+
stroke-width: 1.8;
|
| 1218 |
+
stroke-linecap: round;
|
| 1219 |
+
stroke-linejoin: round;
|
| 1220 |
+
}
|
| 1221 |
+
|
| 1222 |
+
.index-paper-link {
|
| 1223 |
+
margin: 14px 0 30px;
|
| 1224 |
+
font-size: 19px;
|
| 1225 |
+
line-height: 1.35;
|
| 1226 |
+
font-weight: 700;
|
| 1227 |
+
}
|
| 1228 |
+
.index-paper-link a {
|
| 1229 |
+
text-underline-offset: 4px;
|
| 1230 |
+
text-decoration-thickness: 2px;
|
| 1231 |
+
}
|
| 1232 |
+
.art-ico {
|
| 1233 |
+
width: 1em;
|
| 1234 |
+
height: 1em;
|
| 1235 |
+
object-fit: contain;
|
| 1236 |
+
vertical-align: -0.15em;
|
| 1237 |
+
}
|
| 1238 |
+
.art-file-ico {
|
| 1239 |
+
width: 15px;
|
| 1240 |
+
height: 15px;
|
| 1241 |
+
flex: 0 0 auto;
|
| 1242 |
+
fill: none;
|
| 1243 |
+
stroke: currentColor;
|
| 1244 |
+
stroke-width: 1.7;
|
| 1245 |
+
stroke-linecap: round;
|
| 1246 |
+
stroke-linejoin: round;
|
| 1247 |
+
vertical-align: -0.2em;
|
| 1248 |
+
}
|
| 1249 |
+
.out-artifact-ico .art-file-ico {
|
| 1250 |
+
color: var(--muted);
|
| 1251 |
+
}
|
| 1252 |
+
|
| 1253 |
+
/* ---- scroll-to-resource highlight ---- */
|
| 1254 |
+
.res-flash {
|
| 1255 |
+
animation: res-flash 1.5s ease;
|
| 1256 |
+
border-radius: 8px;
|
| 1257 |
+
}
|
| 1258 |
+
@keyframes res-flash {
|
| 1259 |
+
0%,
|
| 1260 |
+
25% {
|
| 1261 |
+
box-shadow: 0 0 0 3px var(--accent);
|
| 1262 |
+
}
|
| 1263 |
+
100% {
|
| 1264 |
+
box-shadow: 0 0 0 3px rgba(249, 115, 22, 0);
|
| 1265 |
+
}
|
| 1266 |
+
}
|
| 1267 |
+
|
| 1268 |
+
/* ---- inline resource chips ---- */
|
| 1269 |
+
#page .res-chip {
|
| 1270 |
+
display: inline-flex;
|
| 1271 |
+
align-items: center;
|
| 1272 |
+
gap: 5px;
|
| 1273 |
+
max-width: 100%;
|
| 1274 |
+
padding: 0 9px 0 6px;
|
| 1275 |
+
margin: 0 1px;
|
| 1276 |
+
border: 1px solid var(--line);
|
| 1277 |
+
border-radius: 999px;
|
| 1278 |
+
background: var(--panel);
|
| 1279 |
+
font-family: var(--mono);
|
| 1280 |
+
font-size: 0.78em;
|
| 1281 |
+
font-weight: 600;
|
| 1282 |
+
color: var(--ink);
|
| 1283 |
+
text-decoration: none;
|
| 1284 |
+
white-space: nowrap;
|
| 1285 |
+
overflow: hidden;
|
| 1286 |
+
text-overflow: ellipsis;
|
| 1287 |
+
vertical-align: middle;
|
| 1288 |
+
line-height: 1.65;
|
| 1289 |
+
transform: translateY(-0.08em);
|
| 1290 |
+
transition: border-color 0.12s, background 0.12s, color 0.12s;
|
| 1291 |
+
}
|
| 1292 |
+
.res-chip-ico {
|
| 1293 |
+
font-size: 1.05em;
|
| 1294 |
+
line-height: 1;
|
| 1295 |
+
}
|
| 1296 |
+
#page .res-chip:hover {
|
| 1297 |
+
border-color: var(--accent);
|
| 1298 |
+
background: var(--accent-soft);
|
| 1299 |
+
color: var(--accent-strong);
|
| 1300 |
+
}
|
| 1301 |
+
|
| 1302 |
+
/* ---- connect footer + modal ---- */
|
| 1303 |
+
#sidebar-foot {
|
| 1304 |
+
margin-top: auto;
|
| 1305 |
+
padding-top: 14px;
|
| 1306 |
+
border-top: 1px solid rgba(255, 255, 255, 0.1);
|
| 1307 |
+
}
|
| 1308 |
+
|
| 1309 |
+
#connect-btn {
|
| 1310 |
+
width: 100%;
|
| 1311 |
+
display: flex;
|
| 1312 |
+
align-items: center;
|
| 1313 |
+
gap: 8px;
|
| 1314 |
+
background: rgba(255, 255, 255, 0.05);
|
| 1315 |
+
color: #c3c4cb;
|
| 1316 |
+
border: 1px solid rgba(255, 255, 255, 0.12);
|
| 1317 |
+
border-radius: 9px;
|
| 1318 |
+
padding: 9px 12px;
|
| 1319 |
+
font-size: 13.5px;
|
| 1320 |
+
font-family: var(--sans);
|
| 1321 |
+
cursor: pointer;
|
| 1322 |
+
transition: background 0.12s, color 0.12s, border-color 0.12s;
|
| 1323 |
+
}
|
| 1324 |
+
#connect-btn:hover {
|
| 1325 |
+
background: rgba(249, 115, 22, 0.14);
|
| 1326 |
+
border-color: rgba(249, 115, 22, 0.4);
|
| 1327 |
+
color: #fdba74;
|
| 1328 |
+
}
|
| 1329 |
+
#connect-btn .ico {
|
| 1330 |
+
font-size: 15px;
|
| 1331 |
+
}
|
| 1332 |
+
|
| 1333 |
+
#modal[hidden] {
|
| 1334 |
+
display: none;
|
| 1335 |
+
}
|
| 1336 |
+
#modal {
|
| 1337 |
+
position: fixed;
|
| 1338 |
+
inset: 0;
|
| 1339 |
+
z-index: 100;
|
| 1340 |
+
display: flex;
|
| 1341 |
+
align-items: center;
|
| 1342 |
+
justify-content: center;
|
| 1343 |
+
padding: 24px;
|
| 1344 |
+
}
|
| 1345 |
+
.modal-backdrop {
|
| 1346 |
+
position: absolute;
|
| 1347 |
+
inset: 0;
|
| 1348 |
+
background: rgba(20, 18, 30, 0.5);
|
| 1349 |
+
backdrop-filter: blur(2px);
|
| 1350 |
+
}
|
| 1351 |
+
.modal-card {
|
| 1352 |
+
position: relative;
|
| 1353 |
+
background: var(--panel);
|
| 1354 |
+
border-radius: 16px;
|
| 1355 |
+
width: 100%;
|
| 1356 |
+
max-width: 620px;
|
| 1357 |
+
max-height: 85vh;
|
| 1358 |
+
overflow-y: auto;
|
| 1359 |
+
box-shadow: 0 24px 70px rgba(20, 15, 50, 0.28);
|
| 1360 |
+
}
|
| 1361 |
+
.modal-head {
|
| 1362 |
+
display: flex;
|
| 1363 |
+
align-items: center;
|
| 1364 |
+
justify-content: space-between;
|
| 1365 |
+
gap: 12px;
|
| 1366 |
+
padding: 18px 22px;
|
| 1367 |
+
border-bottom: 1px solid var(--line);
|
| 1368 |
+
position: sticky;
|
| 1369 |
+
top: 0;
|
| 1370 |
+
background: var(--panel);
|
| 1371 |
+
}
|
| 1372 |
+
.modal-title {
|
| 1373 |
+
display: flex;
|
| 1374 |
+
align-items: center;
|
| 1375 |
+
gap: 10px;
|
| 1376 |
+
font-family: var(--serif);
|
| 1377 |
+
font-size: 21px;
|
| 1378 |
+
letter-spacing: -0.01em;
|
| 1379 |
+
}
|
| 1380 |
+
.modal-logo {
|
| 1381 |
+
width: 26px;
|
| 1382 |
+
height: 26px;
|
| 1383 |
+
object-fit: contain;
|
| 1384 |
+
}
|
| 1385 |
+
.modal-actions {
|
| 1386 |
+
display: flex;
|
| 1387 |
+
align-items: center;
|
| 1388 |
+
gap: 8px;
|
| 1389 |
+
}
|
| 1390 |
+
.btn {
|
| 1391 |
+
font-family: var(--sans);
|
| 1392 |
+
font-size: 13.5px;
|
| 1393 |
+
font-weight: 600;
|
| 1394 |
+
border: 1px solid var(--line);
|
| 1395 |
+
background: var(--panel);
|
| 1396 |
+
color: var(--ink);
|
| 1397 |
+
border-radius: 9px;
|
| 1398 |
+
padding: 8px 13px;
|
| 1399 |
+
cursor: pointer;
|
| 1400 |
+
transition: background 0.12s, border-color 0.12s, color 0.12s;
|
| 1401 |
+
}
|
| 1402 |
+
.btn:hover {
|
| 1403 |
+
border-color: var(--accent);
|
| 1404 |
+
color: var(--accent-strong);
|
| 1405 |
+
}
|
| 1406 |
+
.btn.copied {
|
| 1407 |
+
border-color: #1a8a55;
|
| 1408 |
+
color: #1a8a55;
|
| 1409 |
+
}
|
| 1410 |
+
.btn.icon {
|
| 1411 |
+
font-size: 18px;
|
| 1412 |
+
line-height: 1;
|
| 1413 |
+
padding: 6px 11px;
|
| 1414 |
+
font-weight: 400;
|
| 1415 |
+
}
|
| 1416 |
+
.modal-body {
|
| 1417 |
+
padding: 20px 22px 26px;
|
| 1418 |
+
}
|
| 1419 |
+
.modal-intro {
|
| 1420 |
+
margin: 0 0 20px;
|
| 1421 |
+
color: var(--muted);
|
| 1422 |
+
line-height: 1.55;
|
| 1423 |
+
}
|
| 1424 |
+
#connect-steps {
|
| 1425 |
+
list-style: none;
|
| 1426 |
+
margin: 0;
|
| 1427 |
+
padding: 0;
|
| 1428 |
+
}
|
| 1429 |
+
#connect-steps li {
|
| 1430 |
+
margin-bottom: 18px;
|
| 1431 |
+
}
|
| 1432 |
+
.step-title {
|
| 1433 |
+
font-weight: 600;
|
| 1434 |
+
font-size: 14.5px;
|
| 1435 |
+
margin-bottom: 8px;
|
| 1436 |
+
}
|
| 1437 |
+
.codeblock {
|
| 1438 |
+
display: flex;
|
| 1439 |
+
align-items: center;
|
| 1440 |
+
gap: 8px;
|
| 1441 |
+
background: #17181c;
|
| 1442 |
+
border-radius: 10px;
|
| 1443 |
+
padding: 11px 12px 11px 15px;
|
| 1444 |
+
}
|
| 1445 |
+
.codeblock code {
|
| 1446 |
+
flex: 1;
|
| 1447 |
+
min-width: 0;
|
| 1448 |
+
overflow-x: auto;
|
| 1449 |
+
white-space: nowrap;
|
| 1450 |
+
font-family: var(--mono);
|
| 1451 |
+
font-size: 13px;
|
| 1452 |
+
color: #f0efff;
|
| 1453 |
+
background: none;
|
| 1454 |
+
padding: 0;
|
| 1455 |
+
}
|
| 1456 |
+
.codeblock .copy {
|
| 1457 |
+
flex: 0 0 auto;
|
| 1458 |
+
background: rgba(255, 255, 255, 0.08);
|
| 1459 |
+
color: #c3c4cb;
|
| 1460 |
+
border: 1px solid rgba(255, 255, 255, 0.14);
|
| 1461 |
+
border-radius: 7px;
|
| 1462 |
+
width: 30px;
|
| 1463 |
+
height: 30px;
|
| 1464 |
+
font-size: 14px;
|
| 1465 |
+
cursor: pointer;
|
| 1466 |
+
transition: background 0.12s, color 0.12s;
|
| 1467 |
+
}
|
| 1468 |
+
.codeblock .copy:hover {
|
| 1469 |
+
background: rgba(249, 115, 22, 0.2);
|
| 1470 |
+
color: #fdba74;
|
| 1471 |
+
}
|
| 1472 |
+
.codeblock .copy.copied {
|
| 1473 |
+
color: #52d08a;
|
| 1474 |
+
}
|
| 1475 |
+
|
| 1476 |
+
/* ---- top-level logbook views ---- */
|
| 1477 |
+
#view-tabs {
|
| 1478 |
+
position: sticky;
|
| 1479 |
+
top: 0;
|
| 1480 |
+
z-index: 30;
|
| 1481 |
+
width: 100%;
|
| 1482 |
+
max-width: 1080px;
|
| 1483 |
+
margin: 0 auto 24px;
|
| 1484 |
+
padding-top: 10px;
|
| 1485 |
+
display: flex;
|
| 1486 |
+
align-items: center;
|
| 1487 |
+
justify-content: flex-start;
|
| 1488 |
+
gap: 26px;
|
| 1489 |
+
border-bottom: 1px solid var(--line);
|
| 1490 |
+
background: var(--paper);
|
| 1491 |
+
}
|
| 1492 |
+
#view-tabs a {
|
| 1493 |
+
display: inline-flex;
|
| 1494 |
+
align-items: center;
|
| 1495 |
+
gap: 8px;
|
| 1496 |
+
min-height: 44px;
|
| 1497 |
+
margin-bottom: -1px;
|
| 1498 |
+
color: var(--muted);
|
| 1499 |
+
border-bottom: 2px solid transparent;
|
| 1500 |
+
text-decoration: none;
|
| 1501 |
+
font-size: 13.5px;
|
| 1502 |
+
font-weight: 600;
|
| 1503 |
+
transition: color 0.12s, border-color 0.12s;
|
| 1504 |
+
}
|
| 1505 |
+
#view-tabs a:hover {
|
| 1506 |
+
color: var(--ink);
|
| 1507 |
+
}
|
| 1508 |
+
#view-tabs a.active {
|
| 1509 |
+
color: var(--accent-strong);
|
| 1510 |
+
border-bottom-color: var(--accent);
|
| 1511 |
+
}
|
| 1512 |
+
#view-tabs svg {
|
| 1513 |
+
width: 18px;
|
| 1514 |
+
height: 18px;
|
| 1515 |
+
flex: 0 0 auto;
|
| 1516 |
+
fill: none;
|
| 1517 |
+
stroke: currentColor;
|
| 1518 |
+
stroke-width: 2;
|
| 1519 |
+
stroke-linecap: round;
|
| 1520 |
+
stroke-linejoin: round;
|
| 1521 |
+
}
|
| 1522 |
+
.workspace-file svg,
|
| 1523 |
+
.workspace-folder summary svg,
|
| 1524 |
+
.workspace-download svg {
|
| 1525 |
+
width: 17px;
|
| 1526 |
+
height: 17px;
|
| 1527 |
+
flex: 0 0 auto;
|
| 1528 |
+
fill: none;
|
| 1529 |
+
stroke: currentColor;
|
| 1530 |
+
stroke-width: 1.7;
|
| 1531 |
+
stroke-linecap: round;
|
| 1532 |
+
stroke-linejoin: round;
|
| 1533 |
+
}
|
| 1534 |
+
|
| 1535 |
+
#page.trace-page,
|
| 1536 |
+
#page.workspace-page {
|
| 1537 |
+
max-width: 1080px;
|
| 1538 |
+
}
|
| 1539 |
+
.view-loading {
|
| 1540 |
+
padding: 72px 0;
|
| 1541 |
+
color: var(--muted);
|
| 1542 |
+
text-align: center;
|
| 1543 |
+
}
|
| 1544 |
+
.view-empty {
|
| 1545 |
+
margin: 48px 0;
|
| 1546 |
+
padding: 44px 28px;
|
| 1547 |
+
border: 1px dashed #d8dbe1;
|
| 1548 |
+
border-radius: var(--radius);
|
| 1549 |
+
background: rgba(255, 255, 255, 0.72);
|
| 1550 |
+
text-align: center;
|
| 1551 |
+
}
|
| 1552 |
+
.view-empty h2 {
|
| 1553 |
+
margin: 0 0 7px;
|
| 1554 |
+
font-size: 18px;
|
| 1555 |
+
}
|
| 1556 |
+
.view-empty p {
|
| 1557 |
+
max-width: 560px;
|
| 1558 |
+
margin: 0 auto;
|
| 1559 |
+
color: var(--muted);
|
| 1560 |
+
}
|
| 1561 |
+
.view-empty code {
|
| 1562 |
+
display: inline-block;
|
| 1563 |
+
margin-top: 18px;
|
| 1564 |
+
padding: 7px 10px;
|
| 1565 |
+
border-radius: 7px;
|
| 1566 |
+
background: var(--code-bg);
|
| 1567 |
+
font-family: var(--mono);
|
| 1568 |
+
font-size: 12px;
|
| 1569 |
+
}
|
| 1570 |
+
#page .repo-ref-link {
|
| 1571 |
+
display: inline-block;
|
| 1572 |
+
margin-top: 18px;
|
| 1573 |
+
padding: 8px 14px;
|
| 1574 |
+
border-radius: 8px;
|
| 1575 |
+
background: var(--accent-strong, #2158d0);
|
| 1576 |
+
color: #fff;
|
| 1577 |
+
font-weight: 600;
|
| 1578 |
+
text-decoration: none;
|
| 1579 |
+
}
|
| 1580 |
+
#page .repo-ref-link:hover,
|
| 1581 |
+
#page .repo-ref-link:focus-visible {
|
| 1582 |
+
color: #fff;
|
| 1583 |
+
filter: brightness(0.95);
|
| 1584 |
+
}
|
| 1585 |
+
.view-eyebrow {
|
| 1586 |
+
margin-bottom: 4px;
|
| 1587 |
+
color: var(--accent-strong);
|
| 1588 |
+
font-family: var(--mono);
|
| 1589 |
+
font-size: 11px;
|
| 1590 |
+
font-weight: 700;
|
| 1591 |
+
letter-spacing: 0.12em;
|
| 1592 |
+
text-transform: uppercase;
|
| 1593 |
+
}
|
| 1594 |
+
|
| 1595 |
+
/* ---- trace ---- */
|
| 1596 |
+
.trace-session {
|
| 1597 |
+
scroll-margin-top: 24px;
|
| 1598 |
+
}
|
| 1599 |
+
.trace-session + .trace-session {
|
| 1600 |
+
margin-top: 44px;
|
| 1601 |
+
padding-top: 40px;
|
| 1602 |
+
border-top: 1px solid var(--line);
|
| 1603 |
+
}
|
| 1604 |
+
.trace-session-title {
|
| 1605 |
+
margin: 0 0 14px;
|
| 1606 |
+
color: var(--ink);
|
| 1607 |
+
font-family: var(--serif);
|
| 1608 |
+
font-size: 22px;
|
| 1609 |
+
line-height: 1.2;
|
| 1610 |
+
letter-spacing: -0.02em;
|
| 1611 |
+
overflow-wrap: anywhere;
|
| 1612 |
+
}
|
| 1613 |
+
.workspace-header h1 {
|
| 1614 |
+
margin: 0;
|
| 1615 |
+
color: var(--ink);
|
| 1616 |
+
font-size: 30px;
|
| 1617 |
+
line-height: 1.2;
|
| 1618 |
+
letter-spacing: -0.025em;
|
| 1619 |
+
}
|
| 1620 |
+
.trace-meta {
|
| 1621 |
+
display: flex;
|
| 1622 |
+
flex-wrap: wrap;
|
| 1623 |
+
gap: 9px 20px;
|
| 1624 |
+
margin-bottom: 34px;
|
| 1625 |
+
padding: 14px 16px;
|
| 1626 |
+
border: 1px solid var(--line);
|
| 1627 |
+
border-radius: 10px;
|
| 1628 |
+
background: rgba(255, 255, 255, 0.78);
|
| 1629 |
+
color: var(--muted);
|
| 1630 |
+
font-family: var(--mono);
|
| 1631 |
+
font-size: 11px;
|
| 1632 |
+
}
|
| 1633 |
+
.trace-meta strong {
|
| 1634 |
+
color: var(--ink);
|
| 1635 |
+
font-weight: 650;
|
| 1636 |
+
}
|
| 1637 |
+
.trace-source-missing {
|
| 1638 |
+
color: #b45309;
|
| 1639 |
+
}
|
| 1640 |
+
.trace-timeline {
|
| 1641 |
+
position: relative;
|
| 1642 |
+
}
|
| 1643 |
+
.trace-timeline::before {
|
| 1644 |
+
content: "";
|
| 1645 |
+
position: absolute;
|
| 1646 |
+
top: 0;
|
| 1647 |
+
bottom: 0;
|
| 1648 |
+
left: 82px;
|
| 1649 |
+
width: 1px;
|
| 1650 |
+
background: #dedfe3;
|
| 1651 |
+
}
|
| 1652 |
+
.trace-load-controls {
|
| 1653 |
+
display: flex;
|
| 1654 |
+
align-items: center;
|
| 1655 |
+
justify-content: space-between;
|
| 1656 |
+
gap: 16px;
|
| 1657 |
+
margin: 22px 0 0 100px;
|
| 1658 |
+
padding-top: 16px;
|
| 1659 |
+
border-top: 1px solid var(--line);
|
| 1660 |
+
}
|
| 1661 |
+
.trace-load-progress {
|
| 1662 |
+
color: var(--muted);
|
| 1663 |
+
font-family: var(--mono);
|
| 1664 |
+
font-size: 11px;
|
| 1665 |
+
}
|
| 1666 |
+
.trace-load-more {
|
| 1667 |
+
padding: 7px 12px;
|
| 1668 |
+
border: 1px solid var(--line-strong);
|
| 1669 |
+
border-radius: 7px;
|
| 1670 |
+
background: var(--paper);
|
| 1671 |
+
color: var(--ink);
|
| 1672 |
+
cursor: pointer;
|
| 1673 |
+
font: 650 12px/1.2 var(--sans);
|
| 1674 |
+
}
|
| 1675 |
+
.trace-load-more:hover:not(:disabled) {
|
| 1676 |
+
border-color: var(--accent);
|
| 1677 |
+
color: var(--accent-strong);
|
| 1678 |
+
}
|
| 1679 |
+
.trace-load-more:disabled {
|
| 1680 |
+
cursor: default;
|
| 1681 |
+
opacity: 0.65;
|
| 1682 |
+
}
|
| 1683 |
+
.trace-entry {
|
| 1684 |
+
--trace-depth: 0;
|
| 1685 |
+
position: relative;
|
| 1686 |
+
display: grid;
|
| 1687 |
+
grid-template-columns: 100px minmax(0, 1fr);
|
| 1688 |
+
margin: 0 0 18px calc(var(--trace-depth) * 24px);
|
| 1689 |
+
}
|
| 1690 |
+
.trace-rail {
|
| 1691 |
+
position: relative;
|
| 1692 |
+
min-height: 36px;
|
| 1693 |
+
padding: 4px 28px 0 0;
|
| 1694 |
+
color: #8a8d95;
|
| 1695 |
+
text-align: right;
|
| 1696 |
+
font-family: var(--mono);
|
| 1697 |
+
}
|
| 1698 |
+
.trace-number,
|
| 1699 |
+
.trace-elapsed {
|
| 1700 |
+
display: block;
|
| 1701 |
+
white-space: nowrap;
|
| 1702 |
+
}
|
| 1703 |
+
.trace-number {
|
| 1704 |
+
font-size: 12px;
|
| 1705 |
+
font-weight: 650;
|
| 1706 |
+
}
|
| 1707 |
+
.trace-elapsed {
|
| 1708 |
+
margin-top: 3px;
|
| 1709 |
+
font-size: 10px;
|
| 1710 |
+
}
|
| 1711 |
+
.trace-dot {
|
| 1712 |
+
position: absolute;
|
| 1713 |
+
top: 10px;
|
| 1714 |
+
right: 11px;
|
| 1715 |
+
width: 11px;
|
| 1716 |
+
height: 11px;
|
| 1717 |
+
border: 2px solid var(--paper);
|
| 1718 |
+
border-radius: 50%;
|
| 1719 |
+
background: var(--accent);
|
| 1720 |
+
box-shadow: 0 0 0 1px #d7d9de;
|
| 1721 |
+
}
|
| 1722 |
+
.trace-card {
|
| 1723 |
+
min-width: 0;
|
| 1724 |
+
overflow: hidden;
|
| 1725 |
+
border: 1px solid #dddfe4;
|
| 1726 |
+
border-radius: 11px;
|
| 1727 |
+
background: rgba(255, 255, 255, 0.92);
|
| 1728 |
+
}
|
| 1729 |
+
.trace-card > header {
|
| 1730 |
+
display: flex;
|
| 1731 |
+
align-items: center;
|
| 1732 |
+
gap: 10px;
|
| 1733 |
+
min-height: 37px;
|
| 1734 |
+
padding: 8px 13px;
|
| 1735 |
+
border-bottom: 1px solid #eceef1;
|
| 1736 |
+
}
|
| 1737 |
+
.trace-status .trace-card > header {
|
| 1738 |
+
border-bottom: 0;
|
| 1739 |
+
padding-bottom: 5px;
|
| 1740 |
+
}
|
| 1741 |
+
.trace-kind {
|
| 1742 |
+
font-family: var(--mono);
|
| 1743 |
+
font-size: 10.5px;
|
| 1744 |
+
font-weight: 750;
|
| 1745 |
+
letter-spacing: 0.08em;
|
| 1746 |
+
text-transform: uppercase;
|
| 1747 |
+
}
|
| 1748 |
+
.trace-turn {
|
| 1749 |
+
color: var(--muted);
|
| 1750 |
+
font: 10px var(--mono);
|
| 1751 |
+
}
|
| 1752 |
+
.trace-status-badge {
|
| 1753 |
+
margin-left: auto;
|
| 1754 |
+
padding: 1px 6px;
|
| 1755 |
+
border-radius: 999px;
|
| 1756 |
+
background: #eef0f3;
|
| 1757 |
+
color: var(--muted);
|
| 1758 |
+
font: 9.5px var(--mono);
|
| 1759 |
+
text-transform: uppercase;
|
| 1760 |
+
}
|
| 1761 |
+
.trace-status-badge-error,
|
| 1762 |
+
.trace-status-badge-failed {
|
| 1763 |
+
background: #fef2f2;
|
| 1764 |
+
color: #b91c1c;
|
| 1765 |
+
}
|
| 1766 |
+
.trace-body {
|
| 1767 |
+
margin: 0;
|
| 1768 |
+
padding: 15px 17px 17px;
|
| 1769 |
+
overflow-wrap: anywhere;
|
| 1770 |
+
white-space: pre-wrap;
|
| 1771 |
+
font-family: var(--sans);
|
| 1772 |
+
font-size: 13px;
|
| 1773 |
+
line-height: 1.65;
|
| 1774 |
+
}
|
| 1775 |
+
.trace-reasoning .trace-card {
|
| 1776 |
+
border-style: dashed;
|
| 1777 |
+
border-color: #d7b98a;
|
| 1778 |
+
background: #fffdf8;
|
| 1779 |
+
}
|
| 1780 |
+
.trace-reasoning .trace-kind {
|
| 1781 |
+
color: #9a6b22;
|
| 1782 |
+
}
|
| 1783 |
+
.trace-reasoning .trace-body {
|
| 1784 |
+
font-style: italic;
|
| 1785 |
+
}
|
| 1786 |
+
.trace-user .trace-card {
|
| 1787 |
+
border-left: 3px solid #f3a66d;
|
| 1788 |
+
}
|
| 1789 |
+
.trace-tool_call .trace-card,
|
| 1790 |
+
.trace-tool_result .trace-card {
|
| 1791 |
+
border-color: #2d3036;
|
| 1792 |
+
background: #191a1e;
|
| 1793 |
+
color: #ececf0;
|
| 1794 |
+
}
|
| 1795 |
+
.trace-tool_call .trace-card > header,
|
| 1796 |
+
.trace-tool_result .trace-card > header {
|
| 1797 |
+
border-bottom-color: rgba(255, 255, 255, 0.1);
|
| 1798 |
+
}
|
| 1799 |
+
.trace-tool_call .trace-kind,
|
| 1800 |
+
.trace-tool_result .trace-kind {
|
| 1801 |
+
color: #f5a66d;
|
| 1802 |
+
}
|
| 1803 |
+
.trace-tool_call .trace-turn,
|
| 1804 |
+
.trace-tool_result .trace-turn {
|
| 1805 |
+
color: #979aa3;
|
| 1806 |
+
}
|
| 1807 |
+
.trace-tool_call .trace-body,
|
| 1808 |
+
.trace-tool_result .trace-body,
|
| 1809 |
+
.trace-output pre {
|
| 1810 |
+
font-family: var(--mono);
|
| 1811 |
+
font-size: 11.5px;
|
| 1812 |
+
line-height: 1.6;
|
| 1813 |
+
}
|
| 1814 |
+
#page .trace-tool_call pre.trace-body,
|
| 1815 |
+
#page .trace-tool_result pre.trace-body {
|
| 1816 |
+
margin: 0;
|
| 1817 |
+
padding: 15px 17px 17px;
|
| 1818 |
+
border: 0;
|
| 1819 |
+
border-radius: 0;
|
| 1820 |
+
background: transparent;
|
| 1821 |
+
color: #ececf0;
|
| 1822 |
+
}
|
| 1823 |
+
.trace-output {
|
| 1824 |
+
border-top: 1px dashed rgba(255, 255, 255, 0.14);
|
| 1825 |
+
}
|
| 1826 |
+
.trace-output summary {
|
| 1827 |
+
padding: 9px 14px;
|
| 1828 |
+
color: #aaaeb7;
|
| 1829 |
+
cursor: pointer;
|
| 1830 |
+
font: 700 10px var(--mono);
|
| 1831 |
+
letter-spacing: 0.06em;
|
| 1832 |
+
text-transform: uppercase;
|
| 1833 |
+
}
|
| 1834 |
+
#page .trace-output pre {
|
| 1835 |
+
max-height: 480px;
|
| 1836 |
+
margin: 0;
|
| 1837 |
+
padding: 0 16px 16px;
|
| 1838 |
+
border: 0;
|
| 1839 |
+
border-radius: 0;
|
| 1840 |
+
background: transparent;
|
| 1841 |
+
overflow: auto;
|
| 1842 |
+
color: #d7d8dd;
|
| 1843 |
+
white-space: pre-wrap;
|
| 1844 |
+
}
|
| 1845 |
+
|
| 1846 |
+
/* ---- workspace ---- */
|
| 1847 |
+
.workspace-header {
|
| 1848 |
+
padding-bottom: 24px;
|
| 1849 |
+
}
|
| 1850 |
+
.workspace-header p {
|
| 1851 |
+
margin: 0;
|
| 1852 |
+
color: var(--muted);
|
| 1853 |
+
font-family: var(--mono);
|
| 1854 |
+
font-size: 11px;
|
| 1855 |
+
}
|
| 1856 |
+
.workspace-inventory {
|
| 1857 |
+
overflow: hidden;
|
| 1858 |
+
border: 1px solid var(--line);
|
| 1859 |
+
border-radius: 11px;
|
| 1860 |
+
background: rgba(255, 255, 255, 0.92);
|
| 1861 |
+
}
|
| 1862 |
+
.workspace-folder > summary {
|
| 1863 |
+
display: flex;
|
| 1864 |
+
align-items: center;
|
| 1865 |
+
gap: 8px;
|
| 1866 |
+
min-height: 39px;
|
| 1867 |
+
padding: 8px 13px;
|
| 1868 |
+
background: #fafafa;
|
| 1869 |
+
cursor: pointer;
|
| 1870 |
+
font-weight: 650;
|
| 1871 |
+
list-style: none;
|
| 1872 |
+
}
|
| 1873 |
+
.workspace-folder > summary::-webkit-details-marker {
|
| 1874 |
+
display: none;
|
| 1875 |
+
}
|
| 1876 |
+
.workspace-folder > summary::after {
|
| 1877 |
+
content: "›";
|
| 1878 |
+
margin-left: auto;
|
| 1879 |
+
color: #989ba2;
|
| 1880 |
+
transform: rotate(90deg);
|
| 1881 |
+
}
|
| 1882 |
+
.workspace-folder:not([open]) > summary::after {
|
| 1883 |
+
transform: rotate(0);
|
| 1884 |
+
}
|
| 1885 |
+
.workspace-folder-children {
|
| 1886 |
+
padding-left: 20px;
|
| 1887 |
+
}
|
| 1888 |
+
.workspace-file {
|
| 1889 |
+
display: grid;
|
| 1890 |
+
grid-template-columns: minmax(180px, 1fr) 72px 78px 180px 36px;
|
| 1891 |
+
align-items: center;
|
| 1892 |
+
min-height: 44px;
|
| 1893 |
+
padding: 7px 10px 7px 13px;
|
| 1894 |
+
color: var(--muted);
|
| 1895 |
+
font-family: var(--mono);
|
| 1896 |
+
font-size: 10.5px;
|
| 1897 |
+
}
|
| 1898 |
+
.workspace-file-name {
|
| 1899 |
+
display: flex;
|
| 1900 |
+
align-items: center;
|
| 1901 |
+
min-width: 0;
|
| 1902 |
+
gap: 8px;
|
| 1903 |
+
color: var(--ink);
|
| 1904 |
+
font-family: var(--sans);
|
| 1905 |
+
font-size: 12.5px;
|
| 1906 |
+
font-weight: 550;
|
| 1907 |
+
}
|
| 1908 |
+
.workspace-file-name span {
|
| 1909 |
+
overflow: hidden;
|
| 1910 |
+
text-overflow: ellipsis;
|
| 1911 |
+
white-space: nowrap;
|
| 1912 |
+
}
|
| 1913 |
+
.workspace-file-type {
|
| 1914 |
+
width: fit-content;
|
| 1915 |
+
padding: 1px 6px;
|
| 1916 |
+
border-radius: 999px;
|
| 1917 |
+
background: var(--accent-soft);
|
| 1918 |
+
color: var(--accent-strong);
|
| 1919 |
+
text-transform: uppercase;
|
| 1920 |
+
}
|
| 1921 |
+
.workspace-download {
|
| 1922 |
+
display: inline-flex;
|
| 1923 |
+
align-items: center;
|
| 1924 |
+
justify-content: center;
|
| 1925 |
+
width: 30px;
|
| 1926 |
+
height: 30px;
|
| 1927 |
+
border-radius: 7px;
|
| 1928 |
+
color: var(--muted);
|
| 1929 |
+
}
|
| 1930 |
+
.workspace-download:hover {
|
| 1931 |
+
background: var(--accent-soft);
|
| 1932 |
+
color: var(--accent-strong);
|
| 1933 |
+
}
|
| 1934 |
+
.workspace-unpublished {
|
| 1935 |
+
color: #9ca3af;
|
| 1936 |
+
text-align: center;
|
| 1937 |
+
}
|
| 1938 |
+
|
| 1939 |
+
.workspace-header {
|
| 1940 |
+
display: flex;
|
| 1941 |
+
align-items: center;
|
| 1942 |
+
justify-content: space-between;
|
| 1943 |
+
gap: 16px;
|
| 1944 |
+
flex-wrap: wrap;
|
| 1945 |
+
}
|
| 1946 |
+
.workspace-toggle {
|
| 1947 |
+
display: inline-flex;
|
| 1948 |
+
align-items: center;
|
| 1949 |
+
padding: 2px;
|
| 1950 |
+
border: 1px solid var(--line);
|
| 1951 |
+
border-radius: 999px;
|
| 1952 |
+
background: #fafafa;
|
| 1953 |
+
}
|
| 1954 |
+
.workspace-toggle-btn {
|
| 1955 |
+
padding: 4px 13px;
|
| 1956 |
+
border: 0;
|
| 1957 |
+
border-radius: 999px;
|
| 1958 |
+
background: transparent;
|
| 1959 |
+
color: var(--muted);
|
| 1960 |
+
font-family: var(--sans);
|
| 1961 |
+
font-size: 12px;
|
| 1962 |
+
font-weight: 600;
|
| 1963 |
+
cursor: pointer;
|
| 1964 |
+
}
|
| 1965 |
+
.workspace-toggle-btn:hover {
|
| 1966 |
+
color: var(--accent-strong);
|
| 1967 |
+
}
|
| 1968 |
+
.workspace-toggle-btn.is-active {
|
| 1969 |
+
background: var(--accent);
|
| 1970 |
+
color: #ffffff;
|
| 1971 |
+
}
|
| 1972 |
+
.workspace-group + .workspace-group {
|
| 1973 |
+
margin-top: 18px;
|
| 1974 |
+
}
|
| 1975 |
+
.workspace-group-head,
|
| 1976 |
+
.workspace-hub-group-head {
|
| 1977 |
+
display: flex;
|
| 1978 |
+
align-items: center;
|
| 1979 |
+
gap: 8px;
|
| 1980 |
+
margin: 0;
|
| 1981 |
+
padding: 8px 13px;
|
| 1982 |
+
background: #fafafa;
|
| 1983 |
+
border-bottom: 1px solid var(--line);
|
| 1984 |
+
color: var(--ink);
|
| 1985 |
+
font-family: var(--sans);
|
| 1986 |
+
font-size: 12px;
|
| 1987 |
+
font-weight: 650;
|
| 1988 |
+
text-transform: capitalize;
|
| 1989 |
+
}
|
| 1990 |
+
.workspace-group-count,
|
| 1991 |
+
.workspace-hub-count {
|
| 1992 |
+
padding: 0 7px;
|
| 1993 |
+
border-radius: 999px;
|
| 1994 |
+
background: var(--accent-soft);
|
| 1995 |
+
color: var(--accent-strong);
|
| 1996 |
+
font-family: var(--mono);
|
| 1997 |
+
font-size: 10.5px;
|
| 1998 |
+
}
|
| 1999 |
+
.workspace-group {
|
| 2000 |
+
overflow: hidden;
|
| 2001 |
+
border: 1px solid var(--line);
|
| 2002 |
+
border-radius: 11px;
|
| 2003 |
+
background: rgba(255, 255, 255, 0.92);
|
| 2004 |
+
}
|
| 2005 |
+
|
| 2006 |
+
.workspace-hub {
|
| 2007 |
+
margin-top: 28px;
|
| 2008 |
+
}
|
| 2009 |
+
.workspace-hub-title {
|
| 2010 |
+
margin: 0 0 14px;
|
| 2011 |
+
font-family: var(--sans);
|
| 2012 |
+
font-size: 16px;
|
| 2013 |
+
font-weight: 700;
|
| 2014 |
+
color: var(--ink);
|
| 2015 |
+
}
|
| 2016 |
+
.workspace-hub-group {
|
| 2017 |
+
overflow: hidden;
|
| 2018 |
+
border: 1px solid var(--line);
|
| 2019 |
+
border-radius: 11px;
|
| 2020 |
+
background: rgba(255, 255, 255, 0.92);
|
| 2021 |
+
}
|
| 2022 |
+
.workspace-hub-group + .workspace-hub-group {
|
| 2023 |
+
margin-top: 14px;
|
| 2024 |
+
}
|
| 2025 |
+
.workspace-hub-list {
|
| 2026 |
+
display: flex;
|
| 2027 |
+
flex-direction: column;
|
| 2028 |
+
}
|
| 2029 |
+
.workspace-hub-link {
|
| 2030 |
+
padding: 9px 13px;
|
| 2031 |
+
color: var(--accent-strong);
|
| 2032 |
+
font-family: var(--mono);
|
| 2033 |
+
font-size: 12px;
|
| 2034 |
+
text-decoration: none;
|
| 2035 |
+
overflow: hidden;
|
| 2036 |
+
text-overflow: ellipsis;
|
| 2037 |
+
white-space: nowrap;
|
| 2038 |
+
}
|
| 2039 |
+
.workspace-hub-link + .workspace-hub-link {
|
| 2040 |
+
border-top: 1px solid var(--line);
|
| 2041 |
+
}
|
| 2042 |
+
.workspace-hub-link:hover {
|
| 2043 |
+
background: var(--accent-soft);
|
| 2044 |
+
text-decoration: underline;
|
| 2045 |
+
}
|
| 2046 |
+
|
| 2047 |
+
/* --- UI nits --- */
|
| 2048 |
+
/* Flush group headers: #page h3/h2 (ID selectors) otherwise inject a top margin
|
| 2049 |
+
that, with overflow:hidden on the card, shows as whitespace above "Jobs" etc. */
|
| 2050 |
+
#page .workspace-hub-title {
|
| 2051 |
+
margin: 0 0 14px;
|
| 2052 |
+
}
|
| 2053 |
+
#page .workspace-hub-group-head,
|
| 2054 |
+
#page .workspace-group-head {
|
| 2055 |
+
margin: 0;
|
| 2056 |
+
}
|
| 2057 |
+
/* HF brand logo before the "Hugging Face artifacts" heading */
|
| 2058 |
+
.workspace-hub-title {
|
| 2059 |
+
display: flex;
|
| 2060 |
+
align-items: center;
|
| 2061 |
+
gap: 9px;
|
| 2062 |
+
}
|
| 2063 |
+
.workspace-hub-logo {
|
| 2064 |
+
width: 22px;
|
| 2065 |
+
height: 22px;
|
| 2066 |
+
flex: none;
|
| 2067 |
+
}
|
| 2068 |
+
/* Center empty-state placeholders (heading, body, command) */
|
| 2069 |
+
.view-empty {
|
| 2070 |
+
display: flex;
|
| 2071 |
+
flex-direction: column;
|
| 2072 |
+
align-items: center;
|
| 2073 |
+
}
|
| 2074 |
+
#page .view-empty h2,
|
| 2075 |
+
#page .view-empty p {
|
| 2076 |
+
text-align: center;
|
| 2077 |
+
}
|
| 2078 |
+
|
| 2079 |
+
@media (max-width: 720px) {
|
| 2080 |
+
#app {
|
| 2081 |
+
flex-direction: column;
|
| 2082 |
+
}
|
| 2083 |
+
#sidebar {
|
| 2084 |
+
width: 100%;
|
| 2085 |
+
flex: none;
|
| 2086 |
+
height: auto;
|
| 2087 |
+
position: static;
|
| 2088 |
+
}
|
| 2089 |
+
#content {
|
| 2090 |
+
display: block;
|
| 2091 |
+
width: 100%;
|
| 2092 |
+
padding: 28px 20px 80px;
|
| 2093 |
+
overflow-x: hidden;
|
| 2094 |
+
}
|
| 2095 |
+
#view-tabs {
|
| 2096 |
+
margin: 0 0 20px;
|
| 2097 |
+
gap: 18px;
|
| 2098 |
+
justify-content: flex-start;
|
| 2099 |
+
overflow-x: auto;
|
| 2100 |
+
}
|
| 2101 |
+
#view-tabs a {
|
| 2102 |
+
flex: 0 0 auto;
|
| 2103 |
+
}
|
| 2104 |
+
.trace-timeline::before {
|
| 2105 |
+
left: 16px;
|
| 2106 |
+
}
|
| 2107 |
+
.trace-entry {
|
| 2108 |
+
grid-template-columns: 32px minmax(0, 1fr);
|
| 2109 |
+
margin-left: calc(var(--trace-depth) * 10px);
|
| 2110 |
+
}
|
| 2111 |
+
.trace-rail {
|
| 2112 |
+
padding: 0;
|
| 2113 |
+
}
|
| 2114 |
+
.trace-number,
|
| 2115 |
+
.trace-elapsed {
|
| 2116 |
+
display: none;
|
| 2117 |
+
}
|
| 2118 |
+
.trace-dot {
|
| 2119 |
+
top: 10px;
|
| 2120 |
+
right: 10px;
|
| 2121 |
+
}
|
| 2122 |
+
.workspace-file {
|
| 2123 |
+
grid-template-columns: minmax(150px, 1fr) 66px 34px;
|
| 2124 |
+
}
|
| 2125 |
+
.workspace-file-size,
|
| 2126 |
+
.workspace-file-time {
|
| 2127 |
+
display: none;
|
| 2128 |
+
}
|
| 2129 |
+
#page {
|
| 2130 |
+
width: 100%;
|
| 2131 |
+
max-width: 100%;
|
| 2132 |
+
}
|
| 2133 |
+
#page h1,
|
| 2134 |
+
#logbook-title {
|
| 2135 |
+
font-size: 30px;
|
| 2136 |
+
}
|
| 2137 |
+
.cell-head {
|
| 2138 |
+
align-items: flex-start;
|
| 2139 |
+
flex-direction: column;
|
| 2140 |
+
gap: 4px;
|
| 2141 |
+
}
|
| 2142 |
+
}
|
logbook.js
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
logbook.json
ADDED
|
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": 2,
|
| 3 |
+
"title": "Reproduction: Distributed Direct Preference Optimization",
|
| 4 |
+
"emoji": "🎯",
|
| 5 |
+
"space_id": "SabaPivot/repro-distributed-direct-preference-optimization",
|
| 6 |
+
"paper": {
|
| 7 |
+
"title": "Distributed Direct Preference Optimization",
|
| 8 |
+
"openreview_id": "ljNZyrAlaa",
|
| 9 |
+
"arxiv_id": "2605.20696",
|
| 10 |
+
"url": "https://openreview.net/forum?id=ljNZyrAlaa"
|
| 11 |
+
},
|
| 12 |
+
"tags": [
|
| 13 |
+
"trackio",
|
| 14 |
+
"open-reproductions",
|
| 15 |
+
"icml2026",
|
| 16 |
+
"icml2026-repro",
|
| 17 |
+
"paper-ljNZyrAlaa"
|
| 18 |
+
],
|
| 19 |
+
"updated_at": "2026-07-29T14:48:30+00:00",
|
| 20 |
+
"root": {
|
| 21 |
+
"slug": "index",
|
| 22 |
+
"title": "Reproduction: Distributed Direct Preference Optimization",
|
| 23 |
+
"file": "pages/index.md",
|
| 24 |
+
"children": [
|
| 25 |
+
{
|
| 26 |
+
"slug": "executive-summary",
|
| 27 |
+
"title": "Executive summary",
|
| 28 |
+
"file": "pages/executive-summary/page.md",
|
| 29 |
+
"children": []
|
| 30 |
+
},
|
| 31 |
+
{
|
| 32 |
+
"slug": "claim-1-feddpo-partial-participation-bound",
|
| 33 |
+
"title": "Claim 1: FedDPO partial participation bound",
|
| 34 |
+
"file": "pages/claim-1-feddpo-partial-participation-bound/page.md",
|
| 35 |
+
"children": []
|
| 36 |
+
},
|
| 37 |
+
{
|
| 38 |
+
"slug": "claim-2-full-participation-corollary",
|
| 39 |
+
"title": "Claim 2: Full participation corollary",
|
| 40 |
+
"file": "pages/claim-2-full-participation-corollary/page.md",
|
| 41 |
+
"children": []
|
| 42 |
+
},
|
| 43 |
+
{
|
| 44 |
+
"slug": "claim-3-staleness-penalty",
|
| 45 |
+
"title": "Claim 3: Staleness penalty",
|
| 46 |
+
"file": "pages/claim-3-staleness-penalty/page.md",
|
| 47 |
+
"children": []
|
| 48 |
+
},
|
| 49 |
+
{
|
| 50 |
+
"slug": "claim-4-feddpo-lower-bound",
|
| 51 |
+
"title": "Claim 4: FedDPO lower bound",
|
| 52 |
+
"file": "pages/claim-4-feddpo-lower-bound/page.md",
|
| 53 |
+
"children": []
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"slug": "claim-5-decdpo-spectral-rate",
|
| 57 |
+
"title": "Claim 5: DecDPO spectral rate",
|
| 58 |
+
"file": "pages/claim-5-decdpo-spectral-rate/page.md",
|
| 59 |
+
"children": []
|
| 60 |
+
},
|
| 61 |
+
{
|
| 62 |
+
"slug": "claim-6-shp-numerical-results",
|
| 63 |
+
"title": "Claim 6: SHP numerical results",
|
| 64 |
+
"file": "pages/claim-6-shp-numerical-results/page.md",
|
| 65 |
+
"children": []
|
| 66 |
+
},
|
| 67 |
+
{
|
| 68 |
+
"slug": "conclusion",
|
| 69 |
+
"title": "Conclusion",
|
| 70 |
+
"file": "pages/conclusion/page.md",
|
| 71 |
+
"children": []
|
| 72 |
+
}
|
| 73 |
+
]
|
| 74 |
+
},
|
| 75 |
+
"traces": [],
|
| 76 |
+
"workspace": {
|
| 77 |
+
"file": "workspace.json",
|
| 78 |
+
"file_count": 0,
|
| 79 |
+
"total_size": 0,
|
| 80 |
+
"bucket_id": null
|
| 81 |
+
},
|
| 82 |
+
"agent_view_tokens": 5866,
|
| 83 |
+
"trace_view_tokens": 17229,
|
| 84 |
+
"workspace_view_tokens": 85,
|
| 85 |
+
"revision": "e6d12b1ed894120a273d",
|
| 86 |
+
"traces_ref": {
|
| 87 |
+
"repo_id": "SabaPivot/repro-distributed-direct-preference-optimization-traces",
|
| 88 |
+
"repo_type": "dataset",
|
| 89 |
+
"repo_url": "https://huggingface.co/datasets/SabaPivot/repro-distributed-direct-preference-optimization-traces",
|
| 90 |
+
"private": true,
|
| 91 |
+
"viewer_path": "trackio/index.json"
|
| 92 |
+
},
|
| 93 |
+
"trace_dataset": "https://huggingface.co/datasets/SabaPivot/repro-distributed-direct-preference-optimization-traces",
|
| 94 |
+
"workspace_ref": {
|
| 95 |
+
"repo_id": "SabaPivot/repro-distributed-direct-preference-optimization-artifacts",
|
| 96 |
+
"repo_type": "bucket",
|
| 97 |
+
"repo_url": "https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts",
|
| 98 |
+
"private": true
|
| 99 |
+
},
|
| 100 |
+
"workspace_bucket": "https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts"
|
| 101 |
+
}
|
pages/claim-1-feddpo-partial-participation-bound/page.md
ADDED
|
@@ -0,0 +1,835 @@
|
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|
| 1 |
+
# Claim 1: FedDPO partial participation bound
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_96cda8a2dfb6", "created_at": "2026-07-29T13:20:39+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page tests whether Theorem 5.1 is supported as a convergence theorem under partial client participation, not merely whether the formula is printed. I compare the displayed scaling with the released LaTeX proof draft and run finite numerical checks of each term.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "code", "id": "cell_abd13ff179d8", "created_at": "2026-07-29T13:15:53+00:00", "title": "Run: uv theory_audit.py (exit 0)", "command": ["uv", "run", "theory_audit.py", "--output", "theory_outputs"], "exit_code": 0, "duration_s": 5.768}
|
| 14 |
+
-->
|
| 15 |
+
````bash
|
| 16 |
+
$ uv run theory_audit.py --output theory_outputs
|
| 17 |
+
````
|
| 18 |
+
|
| 19 |
+
exit 0 · 5.8s
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
````python title=theory_audit.py
|
| 23 |
+
# /// script
|
| 24 |
+
# requires-python = ">=3.11"
|
| 25 |
+
# dependencies = [
|
| 26 |
+
# "numpy>=2.0",
|
| 27 |
+
# "plotly>=6.0",
|
| 28 |
+
# "trackio>=0.33.0",
|
| 29 |
+
# ]
|
| 30 |
+
# ///
|
| 31 |
+
"""Independent numerical and consistency audit of the five theory claims.
|
| 32 |
+
|
| 33 |
+
This is not a replacement for a proof. It checks the displayed bounds,
|
| 34 |
+
closed-form spectral recursions, and two logical/proof obligations exposed by
|
| 35 |
+
the paper source.
|
| 36 |
+
"""
|
| 37 |
+
|
| 38 |
+
from __future__ import annotations
|
| 39 |
+
|
| 40 |
+
import argparse
|
| 41 |
+
import csv
|
| 42 |
+
import json
|
| 43 |
+
import math
|
| 44 |
+
from pathlib import Path
|
| 45 |
+
|
| 46 |
+
import numpy as np
|
| 47 |
+
import plotly.graph_objects as go
|
| 48 |
+
from plotly.subplots import make_subplots
|
| 49 |
+
import trackio
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def partial_bound(
|
| 53 |
+
*,
|
| 54 |
+
delta: float,
|
| 55 |
+
eta: float,
|
| 56 |
+
e: int,
|
| 57 |
+
rounds: int,
|
| 58 |
+
clients: int,
|
| 59 |
+
smoothness: float,
|
| 60 |
+
variance: float,
|
| 61 |
+
heterogeneity: float,
|
| 62 |
+
) -> dict[str, float]:
|
| 63 |
+
terms = {
|
| 64 |
+
"optimization": 2.0 * delta / (eta * e * rounds),
|
| 65 |
+
"variance_1_over_s": 8.0 * smoothness * eta * variance / clients,
|
| 66 |
+
"heterogeneity_drift": (
|
| 67 |
+
16.0 * smoothness**2 * eta**2 * e * heterogeneity
|
| 68 |
+
),
|
| 69 |
+
"local_variance": (
|
| 70 |
+
16.0 * smoothness**2 * eta**2 * e**2 * variance / clients
|
| 71 |
+
),
|
| 72 |
+
}
|
| 73 |
+
terms["total"] = sum(terms.values())
|
| 74 |
+
return terms
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
def full_bound(
|
| 78 |
+
*,
|
| 79 |
+
delta: float,
|
| 80 |
+
eta: float,
|
| 81 |
+
e: int,
|
| 82 |
+
rounds: int,
|
| 83 |
+
n_clients: int,
|
| 84 |
+
smoothness: float,
|
| 85 |
+
variance: float,
|
| 86 |
+
heterogeneity: float,
|
| 87 |
+
) -> dict[str, float]:
|
| 88 |
+
terms = {
|
| 89 |
+
"optimization": 2.0 * delta / (eta * e * rounds),
|
| 90 |
+
"averaged_stochastic_variance_1_over_n": (
|
| 91 |
+
2.0 * smoothness * eta * variance / n_clients
|
| 92 |
+
),
|
| 93 |
+
"heterogeneity_drift": (
|
| 94 |
+
8.0 * smoothness**2 * eta**2 * e * heterogeneity
|
| 95 |
+
),
|
| 96 |
+
}
|
| 97 |
+
terms["total"] = sum(terms.values())
|
| 98 |
+
return terms
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
def mixing_matrix(graph_type: str, n: int) -> np.ndarray:
|
| 102 |
+
edges: list[tuple[int, int]]
|
| 103 |
+
if graph_type == "path":
|
| 104 |
+
edges = [(i, i + 1) for i in range(n - 1)]
|
| 105 |
+
elif graph_type == "ring":
|
| 106 |
+
edges = [(i, (i + 1) % n) for i in range(n)]
|
| 107 |
+
elif graph_type == "star":
|
| 108 |
+
edges = [(0, i) for i in range(1, n)]
|
| 109 |
+
elif graph_type == "complete":
|
| 110 |
+
edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
|
| 111 |
+
elif graph_type == "disconnected":
|
| 112 |
+
edges = [(0, 1), (2, 3)]
|
| 113 |
+
else:
|
| 114 |
+
raise ValueError(graph_type)
|
| 115 |
+
|
| 116 |
+
degree = np.zeros(n, dtype=int)
|
| 117 |
+
for i, j in edges:
|
| 118 |
+
degree[i] += 1
|
| 119 |
+
degree[j] += 1
|
| 120 |
+
pi = np.zeros((n, n), dtype=np.float64)
|
| 121 |
+
for i, j in edges:
|
| 122 |
+
weight = 1.0 / (1 + max(degree[i], degree[j]))
|
| 123 |
+
pi[i, j] = weight
|
| 124 |
+
pi[j, i] = weight
|
| 125 |
+
for i in range(n):
|
| 126 |
+
pi[i, i] = 1.0 - pi[i].sum()
|
| 127 |
+
return pi
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
def spectral_row(graph_type: str, n: int, eta: float, injection: float) -> dict:
|
| 131 |
+
pi = mixing_matrix(graph_type, n)
|
| 132 |
+
eigenvalues = np.linalg.eigvalsh(pi)
|
| 133 |
+
ordered = np.sort(np.abs(eigenvalues))[::-1]
|
| 134 |
+
rho = float(ordered[1])
|
| 135 |
+
gap = 1.0 - rho**2
|
| 136 |
+
|
| 137 |
+
if gap > 1e-12:
|
| 138 |
+
closed_form = eta**2 * injection / gap
|
| 139 |
+
error = 0.0
|
| 140 |
+
for _ in range(100_000):
|
| 141 |
+
next_error = rho**2 * error + eta**2 * injection
|
| 142 |
+
if abs(next_error - error) < 1e-15:
|
| 143 |
+
error = next_error
|
| 144 |
+
break
|
| 145 |
+
error = next_error
|
| 146 |
+
relative_error = abs(error - closed_form) / max(abs(closed_form), 1e-30)
|
| 147 |
+
else:
|
| 148 |
+
closed_form = math.inf
|
| 149 |
+
error = 1000 * eta**2 * injection
|
| 150 |
+
relative_error = math.nan
|
| 151 |
+
|
| 152 |
+
return {
|
| 153 |
+
"topology": graph_type,
|
| 154 |
+
"rho": rho,
|
| 155 |
+
"one_minus_rho_sq": gap,
|
| 156 |
+
"inverse_gap": math.inf if gap <= 1e-12 else 1.0 / gap,
|
| 157 |
+
"recursion_limit_numeric": error,
|
| 158 |
+
"recursion_limit_closed_form": closed_form,
|
| 159 |
+
"relative_error": relative_error,
|
| 160 |
+
"row_sum_max_error": float(np.max(np.abs(pi.sum(axis=1) - 1.0))),
|
| 161 |
+
"symmetry_max_error": float(np.max(np.abs(pi - pi.T))),
|
| 162 |
+
}
|
| 163 |
+
|
| 164 |
+
|
| 165 |
+
def write_csv(path: Path, rows: list[dict]) -> None:
|
| 166 |
+
keys = list(rows[0])
|
| 167 |
+
with path.open("w", newline="", encoding="utf-8") as handle:
|
| 168 |
+
writer = csv.DictWriter(handle, fieldnames=keys)
|
| 169 |
+
writer.writeheader()
|
| 170 |
+
writer.writerows(rows)
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
def main() -> None:
|
| 174 |
+
parser = argparse.ArgumentParser()
|
| 175 |
+
parser.add_argument("--output", type=Path, default=Path("theory_outputs"))
|
| 176 |
+
args = parser.parse_args()
|
| 177 |
+
args.output.mkdir(parents=True, exist_ok=True)
|
| 178 |
+
|
| 179 |
+
constants = {
|
| 180 |
+
"delta": 1.0,
|
| 181 |
+
"eta": 0.01,
|
| 182 |
+
"rounds": 1000,
|
| 183 |
+
"n_clients": 5,
|
| 184 |
+
"smoothness": 1.0,
|
| 185 |
+
"variance": 0.4,
|
| 186 |
+
"heterogeneity": 0.2,
|
| 187 |
+
}
|
| 188 |
+
trackio.init(
|
| 189 |
+
project="ddpo-theory-audit",
|
| 190 |
+
name="closed-form-and-recursion",
|
| 191 |
+
config=constants,
|
| 192 |
+
)
|
| 193 |
+
|
| 194 |
+
bound_rows: list[dict] = []
|
| 195 |
+
for e in [1, 3, 6, 12]:
|
| 196 |
+
for clients in [1, 3, 5]:
|
| 197 |
+
terms = partial_bound(
|
| 198 |
+
delta=constants["delta"],
|
| 199 |
+
eta=constants["eta"],
|
| 200 |
+
e=e,
|
| 201 |
+
rounds=constants["rounds"],
|
| 202 |
+
clients=clients,
|
| 203 |
+
smoothness=constants["smoothness"],
|
| 204 |
+
variance=constants["variance"],
|
| 205 |
+
heterogeneity=constants["heterogeneity"],
|
| 206 |
+
)
|
| 207 |
+
bound_rows.append({"E": e, "S": clients, **terms})
|
| 208 |
+
|
| 209 |
+
partial_at_full = partial_bound(
|
| 210 |
+
delta=constants["delta"],
|
| 211 |
+
eta=constants["eta"],
|
| 212 |
+
e=3,
|
| 213 |
+
rounds=constants["rounds"],
|
| 214 |
+
clients=constants["n_clients"],
|
| 215 |
+
smoothness=constants["smoothness"],
|
| 216 |
+
variance=constants["variance"],
|
| 217 |
+
heterogeneity=constants["heterogeneity"],
|
| 218 |
+
)
|
| 219 |
+
tightened_full = full_bound(
|
| 220 |
+
delta=constants["delta"],
|
| 221 |
+
eta=constants["eta"],
|
| 222 |
+
e=3,
|
| 223 |
+
rounds=constants["rounds"],
|
| 224 |
+
n_clients=constants["n_clients"],
|
| 225 |
+
smoothness=constants["smoothness"],
|
| 226 |
+
variance=constants["variance"],
|
| 227 |
+
heterogeneity=constants["heterogeneity"],
|
| 228 |
+
)
|
| 229 |
+
|
| 230 |
+
eta = constants["eta"]
|
| 231 |
+
e = 3
|
| 232 |
+
cq = eta**2 * e * (
|
| 233 |
+
constants["heterogeneity"] + constants["variance"]
|
| 234 |
+
)
|
| 235 |
+
staleness_rows = [
|
| 236 |
+
{
|
| 237 |
+
"q_max": q,
|
| 238 |
+
"C_q": cq,
|
| 239 |
+
"eta_C_q_q_max": eta * cq * q,
|
| 240 |
+
}
|
| 241 |
+
for q in [0, 1, 2, 5, 10]
|
| 242 |
+
]
|
| 243 |
+
|
| 244 |
+
lower_bound_rows = [
|
| 245 |
+
{
|
| 246 |
+
"E_budget": e_budget,
|
| 247 |
+
"claimed_scale_E_kappa_sq_over_S": (
|
| 248 |
+
e_budget
|
| 249 |
+
* constants["heterogeneity"]
|
| 250 |
+
/ 3.0
|
| 251 |
+
),
|
| 252 |
+
"one_step_algorithm_updates_used": 1,
|
| 253 |
+
"one_step_algorithm_is_admissible": True,
|
| 254 |
+
}
|
| 255 |
+
for e_budget in [1, 2, 4, 8, 16]
|
| 256 |
+
]
|
| 257 |
+
|
| 258 |
+
spectral_rows = [
|
| 259 |
+
spectral_row(graph, 5, eta=0.01, injection=0.6)
|
| 260 |
+
for graph in ["path", "ring", "star", "complete", "disconnected"]
|
| 261 |
+
]
|
| 262 |
+
connected_errors = [
|
| 263 |
+
row["relative_error"]
|
| 264 |
+
for row in spectral_rows
|
| 265 |
+
if row["topology"] != "disconnected"
|
| 266 |
+
]
|
| 267 |
+
|
| 268 |
+
proof_obligations = {
|
| 269 |
+
"claim_1_second_moment_counterexample": {
|
| 270 |
+
"description": (
|
| 271 |
+
"The commented proof claims ||sum over S identical gradients||^2 "
|
| 272 |
+
"<= S times the population second moment."
|
| 273 |
+
),
|
| 274 |
+
"S": 3,
|
| 275 |
+
"identical_gradient_norm": 1.0,
|
| 276 |
+
"claimed_lhs": 9.0,
|
| 277 |
+
"claimed_rhs": 3.0,
|
| 278 |
+
"inequality_holds": False,
|
| 279 |
+
},
|
| 280 |
+
"claim_1_constant_reshaping_counterexample": {
|
| 281 |
+
"description": (
|
| 282 |
+
"The commented proof attempts to absorb a non-vanishing "
|
| 283 |
+
"heterogeneity term into O(eta^2 E kappa^2) by choosing eta small."
|
| 284 |
+
),
|
| 285 |
+
"eta_values": [1e-1, 1e-2, 1e-3, 1e-4],
|
| 286 |
+
"fixed_source_term_2_kappa_sq": 2 * constants["heterogeneity"],
|
| 287 |
+
"target_terms_16_eta_sq_E_kappa_sq": [
|
| 288 |
+
16 * value**2 * 3 * constants["heterogeneity"]
|
| 289 |
+
for value in [1e-1, 1e-2, 1e-3, 1e-4]
|
| 290 |
+
],
|
| 291 |
+
"absorption_improves_as_eta_shrinks": False,
|
| 292 |
+
},
|
| 293 |
+
"claim_2_literal_variance_vanishes": {
|
| 294 |
+
"partial_bound_at_S_equals_N": partial_at_full,
|
| 295 |
+
"tightened_full_bound": tightened_full,
|
| 296 |
+
"full_bound_has_nonzero_1_over_N_stochastic_variance": (
|
| 297 |
+
tightened_full["averaged_stochastic_variance_1_over_n"] > 0
|
| 298 |
+
),
|
| 299 |
+
},
|
| 300 |
+
"claim_4_budget_class_nesting": {
|
| 301 |
+
"description": (
|
| 302 |
+
"An algorithm that always uses one update is admissible for every "
|
| 303 |
+
"'at most E' budget. The admissible class expands with E, so its "
|
| 304 |
+
"minimax optimum cannot worsen solely because E is larger."
|
| 305 |
+
),
|
| 306 |
+
"claimed_scale_is_increasing": True,
|
| 307 |
+
"admissible_class_is_nested": True,
|
| 308 |
+
},
|
| 309 |
+
"claim_5_recursion_double_precision": {
|
| 310 |
+
"max_relative_error_connected": max(connected_errors),
|
| 311 |
+
"all_connected_checks_below_1e-10": max(connected_errors) < 1e-10,
|
| 312 |
+
"disconnected_control_has_zero_gap": (
|
| 313 |
+
spectral_rows[-1]["one_minus_rho_sq"] <= 1e-12
|
| 314 |
+
),
|
| 315 |
+
},
|
| 316 |
+
}
|
| 317 |
+
|
| 318 |
+
write_csv(args.output / "bound_terms.csv", bound_rows)
|
| 319 |
+
write_csv(args.output / "staleness_terms.csv", staleness_rows)
|
| 320 |
+
write_csv(args.output / "lower_bound_budget.csv", lower_bound_rows)
|
| 321 |
+
write_csv(args.output / "spectral_recursion.csv", spectral_rows)
|
| 322 |
+
|
| 323 |
+
report = {
|
| 324 |
+
"scope": "numerical/consistency audit, not a proof",
|
| 325 |
+
"constants": constants,
|
| 326 |
+
"partial_bound_rows": bound_rows,
|
| 327 |
+
"staleness_rows": staleness_rows,
|
| 328 |
+
"lower_bound_rows": lower_bound_rows,
|
| 329 |
+
"spectral_rows": spectral_rows,
|
| 330 |
+
"proof_obligations": proof_obligations,
|
| 331 |
+
}
|
| 332 |
+
report_path = args.output / "theory_audit.json"
|
| 333 |
+
report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
|
| 334 |
+
|
| 335 |
+
fig = make_subplots(
|
| 336 |
+
rows=2,
|
| 337 |
+
cols=2,
|
| 338 |
+
subplot_titles=(
|
| 339 |
+
"Displayed FedDPO bound vs local steps",
|
| 340 |
+
"Displayed FedDPO bound vs participation",
|
| 341 |
+
"Staleness term is linear by definition",
|
| 342 |
+
"Consensus recursion vs spectral gap",
|
| 343 |
+
),
|
| 344 |
+
)
|
| 345 |
+
for clients in [1, 3, 5]:
|
| 346 |
+
rows = [row for row in bound_rows if row["S"] == clients]
|
| 347 |
+
fig.add_trace(
|
| 348 |
+
go.Scatter(
|
| 349 |
+
x=[row["E"] for row in rows],
|
| 350 |
+
y=[row["total"] for row in rows],
|
| 351 |
+
mode="lines+markers",
|
| 352 |
+
name=f"S={clients}",
|
| 353 |
+
),
|
| 354 |
+
row=1,
|
| 355 |
+
col=1,
|
| 356 |
+
)
|
| 357 |
+
for e_value in [1, 3, 6, 12]:
|
| 358 |
+
rows = [row for row in bound_rows if row["E"] == e_value]
|
| 359 |
+
fig.add_trace(
|
| 360 |
+
go.Scatter(
|
| 361 |
+
x=[1.0 / row["S"] for row in rows],
|
| 362 |
+
y=[row["total"] for row in rows],
|
| 363 |
+
mode="lines+markers",
|
| 364 |
+
name=f"E={e_value}",
|
| 365 |
+
),
|
| 366 |
+
row=1,
|
| 367 |
+
col=2,
|
| 368 |
+
)
|
| 369 |
+
fig.add_trace(
|
| 370 |
+
go.Scatter(
|
| 371 |
+
x=[row["q_max"] for row in staleness_rows],
|
| 372 |
+
y=[row["eta_C_q_q_max"] for row in staleness_rows],
|
| 373 |
+
mode="lines+markers",
|
| 374 |
+
name="η Cq qmax",
|
| 375 |
+
),
|
| 376 |
+
row=2,
|
| 377 |
+
col=1,
|
| 378 |
+
)
|
| 379 |
+
connected = [
|
| 380 |
+
row for row in spectral_rows if row["topology"] != "disconnected"
|
| 381 |
+
]
|
| 382 |
+
fig.add_trace(
|
| 383 |
+
go.Scatter(
|
| 384 |
+
x=[row["inverse_gap"] for row in connected],
|
| 385 |
+
y=[row["recursion_limit_numeric"] for row in connected],
|
| 386 |
+
mode="markers+text",
|
| 387 |
+
text=[row["topology"] for row in connected],
|
| 388 |
+
textposition="top center",
|
| 389 |
+
name="numeric fixed point",
|
| 390 |
+
),
|
| 391 |
+
row=2,
|
| 392 |
+
col=2,
|
| 393 |
+
)
|
| 394 |
+
fig.update_layout(
|
| 395 |
+
title="Distributed DPO theory: statement-level numerical audit",
|
| 396 |
+
template="plotly_white",
|
| 397 |
+
height=800,
|
| 398 |
+
width=1200,
|
| 399 |
+
)
|
| 400 |
+
fig.update_xaxes(title_text="E", row=1, col=1)
|
| 401 |
+
fig.update_xaxes(title_text="1/S", row=1, col=2)
|
| 402 |
+
fig.update_xaxes(title_text="q_max", row=2, col=1)
|
| 403 |
+
fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
|
| 404 |
+
fig.update_yaxes(title_text="bound", row=1, col=1)
|
| 405 |
+
fig.update_yaxes(title_text="bound", row=1, col=2)
|
| 406 |
+
fig.update_yaxes(title_text="penalty", row=2, col=1)
|
| 407 |
+
fig.update_yaxes(title_text="steady-state error", row=2, col=2)
|
| 408 |
+
figure_path = args.output / "theory_audit.html"
|
| 409 |
+
fig.write_html(figure_path, include_plotlyjs="cdn")
|
| 410 |
+
|
| 411 |
+
for index, row in enumerate(bound_rows):
|
| 412 |
+
trackio.log(
|
| 413 |
+
{
|
| 414 |
+
"bound/total": row["total"],
|
| 415 |
+
"bound/E": row["E"],
|
| 416 |
+
"bound/S": row["S"],
|
| 417 |
+
},
|
| 418 |
+
step=index,
|
| 419 |
+
)
|
| 420 |
+
trackio.log(
|
| 421 |
+
{
|
| 422 |
+
"checks/max_spectral_relative_error": max(connected_errors),
|
| 423 |
+
"checks/claim1_identical_gradient_lhs_over_rhs": 3.0,
|
| 424 |
+
"checks/full_variance_term": tightened_full[
|
| 425 |
+
"averaged_stochastic_variance_1_over_n"
|
| 426 |
+
],
|
| 427 |
+
},
|
| 428 |
+
step=len(bound_rows),
|
| 429 |
+
)
|
| 430 |
+
trackio.log_artifact(
|
| 431 |
+
args.output,
|
| 432 |
+
name="ddpo-theory-audit",
|
| 433 |
+
type="dataset",
|
| 434 |
+
aliases=["reproduction"],
|
| 435 |
+
)
|
| 436 |
+
trackio.finish()
|
| 437 |
+
|
| 438 |
+
print(json.dumps(
|
| 439 |
+
{
|
| 440 |
+
"report": str(report_path),
|
| 441 |
+
"figure": str(figure_path),
|
| 442 |
+
"max_spectral_relative_error": max(connected_errors),
|
| 443 |
+
"claim1_identical_gradient_counterexample": "9 > 3",
|
| 444 |
+
"full_bound_variance_term": tightened_full[
|
| 445 |
+
"averaged_stochastic_variance_1_over_n"
|
| 446 |
+
],
|
| 447 |
+
},
|
| 448 |
+
indent=2,
|
| 449 |
+
))
|
| 450 |
+
|
| 451 |
+
|
| 452 |
+
if __name__ == "__main__":
|
| 453 |
+
main()
|
| 454 |
+
|
| 455 |
+
````
|
| 456 |
+
|
| 457 |
+
|
| 458 |
+
````output
|
| 459 |
+
Installed 31 packages in 161ms
|
| 460 |
+
* Trackio project initialized: ddpo-theory-audit
|
| 461 |
+
* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
|
| 462 |
+
* View dashboard by running in your terminal:
|
| 463 |
+
[1m[38;5;208mtrackio show --project "ddpo-theory-audit"[0m
|
| 464 |
+
* or by running in Python: trackio.show(project="ddpo-theory-audit")
|
| 465 |
+
* Created new run: closed-form-and-recursion
|
| 466 |
+
* Run finished. Uploading logs to Trackio (please wait...)
|
| 467 |
+
{
|
| 468 |
+
"report": "theory_outputs/theory_audit.json",
|
| 469 |
+
"figure": "theory_outputs/theory_audit.html",
|
| 470 |
+
"max_spectral_relative_error": 9.973519694151137e-12,
|
| 471 |
+
"claim1_identical_gradient_counterexample": "9 > 3",
|
| 472 |
+
"full_bound_variance_term": 0.0016
|
| 473 |
+
}
|
| 474 |
+
|
| 475 |
+
````
|
| 476 |
+
|
| 477 |
+
|
| 478 |
+
---
|
| 479 |
+
<!-- trackio-cell
|
| 480 |
+
{"type": "artifact", "id": "cell_2edb6d18dcf1", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: bound_terms.csv", "path": "theory_outputs/bound_terms.csv", "size": 1128, "artifact_type": "dataset", "auto": true}
|
| 481 |
+
-->
|
| 482 |
+
**📦 Artifact** `theory_outputs/bound_terms.csv` · dataset · 1.1 kB
|
| 483 |
+
|
| 484 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/bound_terms.csv
|
| 485 |
+
|
| 486 |
+
|
| 487 |
+
---
|
| 488 |
+
<!-- trackio-cell
|
| 489 |
+
{"type": "artifact", "id": "cell_b16da1f88862", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: spectral_recursion.csv", "path": "theory_outputs/spectral_recursion.csv", "size": 656, "artifact_type": "dataset", "auto": true}
|
| 490 |
+
-->
|
| 491 |
+
**📦 Artifact** `theory_outputs/spectral_recursion.csv` · dataset · 656 B
|
| 492 |
+
|
| 493 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/spectral_recursion.csv
|
| 494 |
+
|
| 495 |
+
|
| 496 |
+
---
|
| 497 |
+
<!-- trackio-cell
|
| 498 |
+
{"type": "artifact", "id": "cell_6e2e147dd868", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: lower_bound_budget.csv", "path": "theory_outputs/lower_bound_budget.csv", "size": 256, "artifact_type": "dataset", "auto": true}
|
| 499 |
+
-->
|
| 500 |
+
**📦 Artifact** `theory_outputs/lower_bound_budget.csv` · dataset · 256 B
|
| 501 |
+
|
| 502 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/lower_bound_budget.csv
|
| 503 |
+
|
| 504 |
+
|
| 505 |
+
---
|
| 506 |
+
<!-- trackio-cell
|
| 507 |
+
{"type": "artifact", "id": "cell_c1c87f178c3b", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: staleness_terms.csv", "path": "theory_outputs/staleness_terms.csv", "size": 251, "artifact_type": "dataset", "auto": true}
|
| 508 |
+
-->
|
| 509 |
+
**📦 Artifact** `theory_outputs/staleness_terms.csv` · dataset · 251 B
|
| 510 |
+
|
| 511 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/staleness_terms.csv
|
| 512 |
+
|
| 513 |
+
|
| 514 |
+
---
|
| 515 |
+
<!-- trackio-cell
|
| 516 |
+
{"type": "markdown", "id": "cell_5f113aecb41a", "created_at": "2026-07-29T13:20:40+00:00", "title": "Finding"}
|
| 517 |
+
-->
|
| 518 |
+
**Verdict: statement present, theorem not verified.** Theorem 5.1 displays
|
| 519 |
+
|
| 520 |
+
`avg_r E||∇L(θ_r)||² ≤ 2Δ/(ηER) + 8Lηζ²_g/S + 16L²η²Eκ² + 16L²η²E²ζ²_g/S`
|
| 521 |
+
|
| 522 |
+
under `η ≤ min{1/(8LE), S/(16LNE)}`. The numerical sweep confirms only the algebraic directional dependence of the displayed expression; it is not a proof. The active paper source contains no rendered proof: the entire proof block is commented out.
|
| 523 |
+
|
| 524 |
+
Two independent blockers appear in that commented draft. First, its Eq. 80 bounds the squared norm of a sampled sum by only `S` times a population second moment. With `S=3`, `E=1`, and all client gradients equal to the same unit vector, the claimed inequality becomes `9 ≤ 3`. Second, draft Eq. 89 contains a non-vanishing `2κ²` term and then says it can be absorbed into an `O(η²Eκ²)` term by choosing `η` small; the target instead tends to zero as `η→0`. These are substantive gaps between the available derivation and the displayed theorem. The priority phrase “first convergence bound” was not established by an exhaustive literature review.
|
| 525 |
+
|
| 526 |
+
Sources: [arXiv 2605.20696](https://arxiv.org/abs/2605.20696), [OpenReview ljNZyrAlaa](https://openreview.net/forum?id=ljNZyrAlaa), [official repository at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).
|
| 527 |
+
|
| 528 |
+
|
| 529 |
+
---
|
| 530 |
+
<!-- trackio-cell
|
| 531 |
+
{"type": "figure", "id": "cell_186d7c55c977", "created_at": "2026-07-29T13:22:51+00:00", "title": "Bound and proof-obligation audit"}
|
| 532 |
+
-->
|
| 533 |
+
````html
|
| 534 |
+
<html>
|
| 535 |
+
<head><meta charset="utf-8" /></head>
|
| 536 |
+
<body>
|
| 537 |
+
<div style="height:800px; width:1200px;"> <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
|
| 538 |
+
<script charset="utf-8" src="https://cdn.plot.ly/plotly-3.7.0.min.js" integrity="sha256-jvTGqxNp8AGWEcvNLVuKr+8j5dGe9Yw51LQkmDH+IYA=" crossorigin="anonymous"></script> <div id="2cfc58e2-9925-4649-9835-b357bd7e6f2e" class="plotly-graph-div" style="height:100%; width:100%;"></div> <script> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("2cfc58e2-9925-4649-9835-b357bd7e6f2e")) { Plotly.newPlot( "2cfc58e2-9925-4649-9835-b357bd7e6f2e", 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</body>
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</html>
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````
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| 730 |
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"recursion_limit_numeric": 0.0002516428979856393,
|
| 731 |
+
"recursion_limit_closed_form": 0.00025164289798814906,
|
| 732 |
+
"relative_error": 9.973519694151137e-12,
|
| 733 |
+
"row_sum_max_error": 0.0,
|
| 734 |
+
"symmetry_max_error": 0.0
|
| 735 |
+
},
|
| 736 |
+
{
|
| 737 |
+
"topology": "ring",
|
| 738 |
+
"rho": 0.5393446629166316,
|
| 739 |
+
"one_minus_rho_sq": 0.709107334583345,
|
| 740 |
+
"inverse_gap": 1.4102237436136194,
|
| 741 |
+
"recursion_limit_numeric": 8.461342461668239e-05,
|
| 742 |
+
"recursion_limit_closed_form": 8.461342461681716e-05,
|
| 743 |
+
"relative_error": 1.5927298847701173e-12,
|
| 744 |
+
"row_sum_max_error": 0.0,
|
| 745 |
+
"symmetry_max_error": 0.0
|
| 746 |
+
},
|
| 747 |
+
{
|
| 748 |
+
"topology": "star",
|
| 749 |
+
"rho": 0.8,
|
| 750 |
+
"one_minus_rho_sq": 0.3599999999999999,
|
| 751 |
+
"inverse_gap": 2.7777777777777786,
|
| 752 |
+
"recursion_limit_numeric": 0.00016666666666517355,
|
| 753 |
+
"recursion_limit_closed_form": 0.00016666666666666672,
|
| 754 |
+
"relative_error": 8.958979391682217e-12,
|
| 755 |
+
"row_sum_max_error": 0.0,
|
| 756 |
+
"symmetry_max_error": 0.0
|
| 757 |
+
},
|
| 758 |
+
{
|
| 759 |
+
"topology": "complete",
|
| 760 |
+
"rho": 8.284195946896153e-17,
|
| 761 |
+
"one_minus_rho_sq": 1.0,
|
| 762 |
+
"inverse_gap": 1.0,
|
| 763 |
+
"recursion_limit_numeric": 6e-05,
|
| 764 |
+
"recursion_limit_closed_form": 6e-05,
|
| 765 |
+
"relative_error": 0.0,
|
| 766 |
+
"row_sum_max_error": 0.0,
|
| 767 |
+
"symmetry_max_error": 0.0
|
| 768 |
+
},
|
| 769 |
+
{
|
| 770 |
+
"topology": "disconnected",
|
| 771 |
+
"rho": 1.0,
|
| 772 |
+
"one_minus_rho_sq": 0.0,
|
| 773 |
+
"inverse_gap": Infinity,
|
| 774 |
+
"recursion_limit_numeric": 0.06,
|
| 775 |
+
"recursion_limit_closed_form": Infinity,
|
| 776 |
+
"relative_error": NaN,
|
| 777 |
+
"row_sum_max_error": 0.0,
|
| 778 |
+
"symmetry_max_error": 0.0
|
| 779 |
+
}
|
| 780 |
+
],
|
| 781 |
+
"proof_obligations": {
|
| 782 |
+
"claim_1_second_moment_counterexample": {
|
| 783 |
+
"description": "The commented proof claims ||sum over S identical gradients||^2 <= S times the population second moment.",
|
| 784 |
+
"S": 3,
|
| 785 |
+
"identical_gradient_norm": 1.0,
|
| 786 |
+
"claimed_lhs": 9.0,
|
| 787 |
+
"claimed_rhs": 3.0,
|
| 788 |
+
"inequality_holds": false
|
| 789 |
+
},
|
| 790 |
+
"claim_1_constant_reshaping_counterexample": {
|
| 791 |
+
"description": "The commented proof attempts to absorb a non-vanishing heterogeneity term into O(eta^2 E kappa^2) by choosing eta small.",
|
| 792 |
+
"eta_values": [
|
| 793 |
+
0.1,
|
| 794 |
+
0.01,
|
| 795 |
+
0.001,
|
| 796 |
+
0.0001
|
| 797 |
+
],
|
| 798 |
+
"fixed_source_term_2_kappa_sq": 0.4,
|
| 799 |
+
"target_terms_16_eta_sq_E_kappa_sq": [
|
| 800 |
+
0.09600000000000003,
|
| 801 |
+
0.0009600000000000001,
|
| 802 |
+
9.600000000000001e-06,
|
| 803 |
+
9.600000000000001e-08
|
| 804 |
+
],
|
| 805 |
+
"absorption_improves_as_eta_shrinks": false
|
| 806 |
+
},
|
| 807 |
+
"claim_2_literal_variance_vanishes": {
|
| 808 |
+
"partial_bound_at_S_equals_N": {
|
| 809 |
+
"optimization": 0.06666666666666667,
|
| 810 |
+
"variance_1_over_s": 0.0064,
|
| 811 |
+
"heterogeneity_drift": 0.0009600000000000001,
|
| 812 |
+
"local_variance": 0.0011520000000000002,
|
| 813 |
+
"total": 0.07517866666666667
|
| 814 |
+
},
|
| 815 |
+
"tightened_full_bound": {
|
| 816 |
+
"optimization": 0.06666666666666667,
|
| 817 |
+
"averaged_stochastic_variance_1_over_n": 0.0016,
|
| 818 |
+
"heterogeneity_drift": 0.00048000000000000007,
|
| 819 |
+
"total": 0.06874666666666666
|
| 820 |
+
},
|
| 821 |
+
"full_bound_has_nonzero_1_over_N_stochastic_variance": true
|
| 822 |
+
},
|
| 823 |
+
"claim_4_budget_class_nesting": {
|
| 824 |
+
"description": "An algorithm that always uses one update is admissible for every 'at most E' budget. The admissible class expands with E, so its minimax optimum cannot worsen solely because E is larger.",
|
| 825 |
+
"claimed_scale_is_increasing": true,
|
| 826 |
+
"admissible_class_is_nested": true
|
| 827 |
+
},
|
| 828 |
+
"claim_5_recursion_double_precision": {
|
| 829 |
+
"max_relative_error_connected": 9.973519694151137e-12,
|
| 830 |
+
"all_connected_checks_below_1e-10": true,
|
| 831 |
+
"disconnected_control_has_zero_gap": true
|
| 832 |
+
}
|
| 833 |
+
}
|
| 834 |
+
}
|
| 835 |
+
````
|
pages/claim-2-full-participation-corollary/page.md
ADDED
|
@@ -0,0 +1,21 @@
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|
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|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
| 1 |
+
# Claim 2: Full participation corollary
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_85aa020bf060", "created_at": "2026-07-29T13:20:42+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page checks the literal Claim 2 wording against Corollary 5.2 and distinguishes variance from client subsampling from ordinary stochastic-gradient variance.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "markdown", "id": "cell_14530d6f43de", "created_at": "2026-07-29T13:20:43+00:00", "title": "Finding"}
|
| 14 |
+
-->
|
| 15 |
+
**Verdict: contradicted as literally worded; a narrower interpretation is supported.** Corollary 5.2 states
|
| 16 |
+
|
| 17 |
+
`avg_r E||∇L(θ_r)||² ≤ 2Δ/(ηER) + 2Lηζ²_g/N + 8L²η²Eκ²`.
|
| 18 |
+
|
| 19 |
+
Thus the partial-participation terms `8Lηζ²_g/S` and `16L²η²E²ζ²_g/S` do not survive in their Theorem 5.1 form, so the extra variance from sampling clients disappears. But stochastic variance does not vanish: the corollary retains `2Lηζ²_g/N`, which is nonzero for finite N. In the audit constants `L=1`, `η=0.01`, `ζ²_g=0.4`, `N=5`, this retained term is `0.0016`. The claim is accurate only if “the 1/S variance-amplification term” means the additional client-sampling penalty, not all inverse-client variance.
|
| 20 |
+
|
| 21 |
+
Source: [paper](https://arxiv.org/abs/2605.20696) and [official code at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).
|
pages/claim-3-staleness-penalty/page.md
ADDED
|
@@ -0,0 +1,19 @@
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|
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|
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|
|
|
|
| 1 |
+
# Claim 3: Staleness penalty
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_594b4addacaa", "created_at": "2026-07-29T13:20:45+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page checks the Theorem 5.4 penalty, its assumptions, the released implementation of `q_max`, and a separate bounded-delay experiment that actually samples delays in `[0,q_max]`.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "markdown", "id": "cell_0fdfaf3f9193", "created_at": "2026-07-29T13:20:47+00:00", "title": "Finding"}
|
| 14 |
+
-->
|
| 15 |
+
**Verdict: the stated term is present and the mechanism is numerically supported, but the theorem is not verified.** Theorem 5.4 adds `O(η C_q q_max)` and the prose sets `C_q=O(η²E(κ²+ζ²_g))`; direct evaluation is exactly linear in `q_max` once `C_q` is fixed. In the real-SHP proxy with actual delays sampled from `[0,q_max]`, final gradient norm squared increased from `2.9570157e-4` at `q=0` to `2.9586910e-4` at `q=2` and `2.9603010e-4` at `q=5`; the linear fit has positive slope `6.48e-8` per delay round and `R²=0.984`.
|
| 16 |
+
|
| 17 |
+
The proof is commented out. Assumption 5.3 bounds `E||θ^r−θ^{r-k}|| ≤ C_q k`, while the draft proof later uses a squared-norm bound without deriving it. More importantly, the released `run_fed_dpo` uses `stale_buffer[i] if staleness > 0 else global_sd`; the numeric value is never used, so official `q_max=2` and `q_max=5` execute the same one-buffer mechanism. Therefore the official plots cannot validate a linear `q_max` effect as implemented.
|
| 18 |
+
|
| 19 |
+
Resources: [SHP](https://huggingface.co/datasets/stanfordnlp/SHP), [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2), and [audited algorithms.py](https://github.com/starkjiang/distributed_dpo/blob/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7/distributed_dpo/algorithms.py).
|
pages/claim-4-feddpo-lower-bound/page.md
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Claim 4: FedDPO lower bound
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_ea4df74361fa", "created_at": "2026-07-29T13:20:49+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page checks whether Theorem 5.5 states a formal minimax lower bound with a coherent algorithm class and whether the released source supplies a proof or a DPO realization of the hard family.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "markdown", "id": "cell_713a73667008", "created_at": "2026-07-29T13:20:50+00:00", "title": "Finding"}
|
| 14 |
+
-->
|
| 15 |
+
**Verdict: unsupported and likely false as stated.** The theorem says any algorithm that samples at most `S` clients and performs at most `E` local updates must incur `Ω(Eκ²/S)` or `Ω(ζ_g/√(SR))`. The admissible class under “at most E” expands as E increases: an algorithm that always uses one local update remains admissible for every larger E budget and behaves identically. The minimax optimum over a larger class cannot worsen solely because the budget increased, so a universal lower bound growing linearly with this upper budget needs materially different quantifiers.
|
| 16 |
+
|
| 17 |
+
No active proof is included. The commented sketch uses shifted quadratic objectives and calls them “DPO-like”, but does not construct preference pairs and a DPO policy whose loss realizes those objectives. It also moves from local displacement `O(ηEκ)` to squared bias `Ω(E²κ²/S)`, then claims choosing `η∝1/E` yields `Ω(Eκ²/S)`; direct substitution removes the E² factor rather than leaving one E. These issues prevent verification of Claim 4.
|
| 18 |
+
|
| 19 |
+
Source: [paper](https://arxiv.org/abs/2605.20696) and [source-linked official repository](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).
|
pages/claim-5-decdpo-spectral-rate/page.md
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Claim 5: DecDPO spectral rate
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_30089a9dbcbf", "created_at": "2026-07-29T13:20:52+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page checks the algebra from the displayed Theorem 6.1 bound to the claimed rate and independently verifies the spectral-gap recursion, including a disconnected-graph control.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "markdown", "id": "cell_79e71a709ca5", "created_at": "2026-07-29T13:20:54+00:00", "title": "Finding"}
|
| 14 |
+
-->
|
| 15 |
+
**Verdict: displayed statement and rate algebra supported; theorem proof not verified.** Theorem 6.1 displays
|
| 16 |
+
|
| 17 |
+
`avg_r E||∇L(θ̄_r)||² ≤ 2Δ/(ηR) + 32L²η²ζ²_g/(1−ρ²) + 16L²η²κ²/(1−ρ²)`
|
| 18 |
+
|
| 19 |
+
with `η≤√(1−ρ²)/(4L)`. Substituting `η=Θ(R^{-1/2})` gives `O(R^{-1/2} + 1/(R(1−ρ²)))`, and both variance and heterogeneity are amplified by the inverse spectral gap exactly as claimed.
|
| 20 |
+
|
| 21 |
+
For the scalar consensus recursion `e_{r+1}=ρ²e_r+η²v`, all four connected N=5 Metropolis graphs converged to the closed form `η²v/(1−ρ²)` with maximum relative error `9.97e-12`. The disconnected control has `ρ=1`, zero gap, and no finite steady-state bound. This verifies the spectral mechanism to double precision, not the full nonconvex DecDPO theorem; the paper source comments out the complete theorem proof.
|
| 22 |
+
|
| 23 |
+
Resources: [paper](https://arxiv.org/abs/2605.20696) and [official topology implementation](https://github.com/starkjiang/distributed_dpo/blob/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7/distributed_dpo/algorithms.py).
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
---
|
| 27 |
+
<!-- trackio-cell
|
| 28 |
+
{"type": "figure", "id": "cell_76d3edbeea9b", "created_at": "2026-07-29T13:22:53+00:00", "title": "Spectral recursion audit"}
|
| 29 |
+
-->
|
| 30 |
+
````html
|
| 31 |
+
<html>
|
| 32 |
+
<head><meta charset="utf-8" /></head>
|
| 33 |
+
<body>
|
| 34 |
+
<div style="height:800px; width:1200px;"> <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
|
| 35 |
+
<script charset="utf-8" src="https://cdn.plot.ly/plotly-3.7.0.min.js" integrity="sha256-jvTGqxNp8AGWEcvNLVuKr+8j5dGe9Yw51LQkmDH+IYA=" crossorigin="anonymous"></script> <div id="2cfc58e2-9925-4649-9835-b357bd7e6f2e" class="plotly-graph-div" style="height:100%; width:100%;"></div> <script> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("2cfc58e2-9925-4649-9835-b357bd7e6f2e")) { Plotly.newPlot( "2cfc58e2-9925-4649-9835-b357bd7e6f2e", 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|
| 36 |
+
</body>
|
| 37 |
+
</html>
|
| 38 |
+
````
|
| 39 |
+
|
| 40 |
+
````raw
|
| 41 |
+
topology,rho,one_minus_rho_sq,inverse_gap,recursion_limit_numeric,recursion_limit_closed_form,relative_error,row_sum_max_error,symmetry_max_error
|
| 42 |
+
path,0.872677996249965,0.23843311486114604,4.194048299802485,0.0002516428979856393,0.00025164289798814906,9.973519694151137e-12,0.0,0.0
|
| 43 |
+
ring,0.5393446629166316,0.709107334583345,1.4102237436136194,8.461342461668239e-05,8.461342461681716e-05,1.5927298847701173e-12,0.0,0.0
|
| 44 |
+
star,0.8,0.3599999999999999,2.7777777777777786,0.00016666666666517355,0.00016666666666666672,8.958979391682217e-12,0.0,0.0
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| 45 |
+
complete,8.284195946896153e-17,1.0,1.0,6e-05,6e-05,0.0,0.0,0.0
|
| 46 |
+
disconnected,1.0,0.0,inf,0.06,inf,nan,0.0,0.0
|
| 47 |
+
|
| 48 |
+
````
|
pages/claim-6-shp-numerical-results/page.md
ADDED
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|
| 1 |
+
# Claim 6: SHP numerical results
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_c4f1bdc9153c", "created_at": "2026-07-29T13:21:24+00:00", "title": "Test plan"}
|
| 7 |
+
-->
|
| 8 |
+
This page attempts the paper Section 7 ablations with real SHP data and N=5. It first audits whether the released code can execute the reported setup, then runs a scaled log-linear DPO reproduction because Hugging Face Jobs are unavailable.
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
<!-- trackio-cell
|
| 13 |
+
{"type": "code", "id": "cell_d65a71cc1cf2", "created_at": "2026-07-29T13:17:01+00:00", "title": "Run: uv empirical_repro.py (exit 0)", "command": ["uv", "run", "empirical_repro.py", "--output", "empirical_outputs", "--rounds", "80", "--seeds", "5", "--dimension", "1024", "--rows-per-domain", "160", "--train-rows", "120"], "exit_code": 0, "duration_s": 58.641}
|
| 14 |
+
-->
|
| 15 |
+
````bash
|
| 16 |
+
$ uv run empirical_repro.py --output empirical_outputs --rounds 80 --seeds 5 --dimension 1024 --rows-per-domain 160 --train-rows 120
|
| 17 |
+
````
|
| 18 |
+
|
| 19 |
+
exit 0 · 58.6s
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
````python title=empirical_repro.py
|
| 23 |
+
# /// script
|
| 24 |
+
# requires-python = ">=3.11"
|
| 25 |
+
# dependencies = [
|
| 26 |
+
# "duckdb>=1.3",
|
| 27 |
+
# "numpy>=2.0",
|
| 28 |
+
# "plotly>=6.0",
|
| 29 |
+
# "trackio>=0.33.0",
|
| 30 |
+
# ]
|
| 31 |
+
# ///
|
| 32 |
+
"""Scaled SHP reproduction using a log-linear DPO policy.
|
| 33 |
+
|
| 34 |
+
The paper's GPU experiment uses DistilGPT-2. Because the Hub Jobs canary is
|
| 35 |
+
blocked by account credit (HTTP 402), this local fallback uses the same N=5
|
| 36 |
+
client structure and real SHP preference pairs but a 1024-dimensional hashed
|
| 37 |
+
log-linear policy. This directly matches the policy class of the theory and
|
| 38 |
+
is intentionally labelled a scaled proxy, not a full LLM replication.
|
| 39 |
+
"""
|
| 40 |
+
|
| 41 |
+
from __future__ import annotations
|
| 42 |
+
|
| 43 |
+
import argparse
|
| 44 |
+
import csv
|
| 45 |
+
import hashlib
|
| 46 |
+
import json
|
| 47 |
+
import math
|
| 48 |
+
import random
|
| 49 |
+
import re
|
| 50 |
+
import time
|
| 51 |
+
from collections import defaultdict
|
| 52 |
+
from pathlib import Path
|
| 53 |
+
|
| 54 |
+
import duckdb
|
| 55 |
+
import numpy as np
|
| 56 |
+
import plotly.graph_objects as go
|
| 57 |
+
from plotly.subplots import make_subplots
|
| 58 |
+
import trackio
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
PARQUET_URLS = [
|
| 62 |
+
"https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
|
| 63 |
+
"refs%2Fconvert%2Fparquet/default/train/0000.parquet",
|
| 64 |
+
"https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
|
| 65 |
+
"refs%2Fconvert%2Fparquet/default/train/0001.parquet",
|
| 66 |
+
]
|
| 67 |
+
DOMAINS = [
|
| 68 |
+
"askacademia_train",
|
| 69 |
+
"askbaking_train",
|
| 70 |
+
"askcarguys_train",
|
| 71 |
+
"askphilosophy_train",
|
| 72 |
+
"legaladvice_train",
|
| 73 |
+
]
|
| 74 |
+
TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_'-]{1,}")
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
def load_shp(rows_per_domain: int) -> dict[int, list[dict]]:
|
| 78 |
+
urls = ", ".join(f"'{url}'" for url in PARQUET_URLS)
|
| 79 |
+
domains = ", ".join(f"'{domain}'" for domain in DOMAINS)
|
| 80 |
+
query = f"""
|
| 81 |
+
WITH ranked AS (
|
| 82 |
+
SELECT
|
| 83 |
+
domain,
|
| 84 |
+
human_ref_A,
|
| 85 |
+
human_ref_B,
|
| 86 |
+
score_A,
|
| 87 |
+
score_B,
|
| 88 |
+
ROW_NUMBER() OVER (PARTITION BY domain ORDER BY post_id, c_root_id_A, c_root_id_B) AS rn
|
| 89 |
+
FROM read_parquet([{urls}])
|
| 90 |
+
WHERE domain IN ({domains}) AND score_A != score_B
|
| 91 |
+
)
|
| 92 |
+
SELECT domain, human_ref_A, human_ref_B, score_A, score_B
|
| 93 |
+
FROM ranked
|
| 94 |
+
WHERE rn <= {int(rows_per_domain)}
|
| 95 |
+
ORDER BY domain, rn
|
| 96 |
+
"""
|
| 97 |
+
rows = duckdb.sql(query).fetchall()
|
| 98 |
+
grouped: dict[str, list[dict]] = defaultdict(list)
|
| 99 |
+
for domain, text_a, text_b, score_a, score_b in rows:
|
| 100 |
+
if score_a > score_b:
|
| 101 |
+
chosen, rejected = text_a, text_b
|
| 102 |
+
else:
|
| 103 |
+
chosen, rejected = text_b, text_a
|
| 104 |
+
if chosen and rejected and chosen != rejected:
|
| 105 |
+
grouped[domain].append(
|
| 106 |
+
{"chosen": chosen[-500:], "rejected": rejected[-500:]}
|
| 107 |
+
)
|
| 108 |
+
missing = [domain for domain in DOMAINS if len(grouped[domain]) < rows_per_domain]
|
| 109 |
+
if missing:
|
| 110 |
+
raise RuntimeError(f"insufficient rows for domains: {missing}")
|
| 111 |
+
return {index: grouped[domain] for index, domain in enumerate(DOMAINS)}
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
def stable_bucket(token: str, dimension: int) -> tuple[int, float]:
|
| 115 |
+
digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
|
| 116 |
+
value = int.from_bytes(digest, "little")
|
| 117 |
+
return value % dimension, 1.0 if (value >> 63) == 0 else -1.0
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def text_vector(text: str, dimension: int) -> np.ndarray:
|
| 121 |
+
vector = np.zeros(dimension, dtype=np.float64)
|
| 122 |
+
tokens = TOKEN_RE.findall(text.lower())
|
| 123 |
+
for token in tokens:
|
| 124 |
+
index, sign = stable_bucket(token, dimension)
|
| 125 |
+
vector[index] += sign
|
| 126 |
+
norm = np.linalg.norm(vector)
|
| 127 |
+
if norm > 0:
|
| 128 |
+
vector /= norm
|
| 129 |
+
return vector
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
def featurize(
|
| 133 |
+
data: dict[int, list[dict]],
|
| 134 |
+
dimension: int,
|
| 135 |
+
) -> dict[int, np.ndarray]:
|
| 136 |
+
result = {}
|
| 137 |
+
for client, rows in data.items():
|
| 138 |
+
result[client] = np.stack(
|
| 139 |
+
[
|
| 140 |
+
text_vector(row["chosen"], dimension)
|
| 141 |
+
- text_vector(row["rejected"], dimension)
|
| 142 |
+
for row in rows
|
| 143 |
+
]
|
| 144 |
+
)
|
| 145 |
+
return result
|
| 146 |
+
|
| 147 |
+
|
| 148 |
+
def sigmoid_negative(z: np.ndarray) -> np.ndarray:
|
| 149 |
+
return np.where(
|
| 150 |
+
z >= 0,
|
| 151 |
+
np.exp(-z) / (1.0 + np.exp(-z)),
|
| 152 |
+
1.0 / (1.0 + np.exp(z)),
|
| 153 |
+
)
|
| 154 |
+
|
| 155 |
+
|
| 156 |
+
def batch_gradient(
|
| 157 |
+
theta: np.ndarray,
|
| 158 |
+
features: np.ndarray,
|
| 159 |
+
beta: float,
|
| 160 |
+
) -> np.ndarray:
|
| 161 |
+
z = beta * (features @ theta)
|
| 162 |
+
weights = -beta * sigmoid_negative(z)
|
| 163 |
+
return (weights[:, None] * features).mean(axis=0)
|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
def batch_loss(theta: np.ndarray, features: np.ndarray, beta: float) -> float:
|
| 167 |
+
z = beta * (features @ theta)
|
| 168 |
+
return float(np.logaddexp(0.0, -z).mean())
|
| 169 |
+
|
| 170 |
+
|
| 171 |
+
def evaluate(
|
| 172 |
+
theta: np.ndarray,
|
| 173 |
+
eval_features: dict[int, np.ndarray],
|
| 174 |
+
beta: float,
|
| 175 |
+
) -> dict[str, float]:
|
| 176 |
+
pooled = np.concatenate(list(eval_features.values()), axis=0)
|
| 177 |
+
global_gradient = batch_gradient(theta, pooled, beta)
|
| 178 |
+
client_gradients = np.stack(
|
| 179 |
+
[batch_gradient(theta, rows, beta) for rows in eval_features.values()]
|
| 180 |
+
)
|
| 181 |
+
heterogeneity = float(
|
| 182 |
+
np.mean(np.sum((client_gradients - global_gradient) ** 2, axis=1))
|
| 183 |
+
)
|
| 184 |
+
return {
|
| 185 |
+
"loss": batch_loss(theta, pooled, beta),
|
| 186 |
+
"grad_norm_sq": float(global_gradient @ global_gradient),
|
| 187 |
+
"kappa_sq_proxy": heterogeneity,
|
| 188 |
+
}
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
def local_update(
|
| 192 |
+
start: np.ndarray,
|
| 193 |
+
features: np.ndarray,
|
| 194 |
+
*,
|
| 195 |
+
steps: int,
|
| 196 |
+
batch_size: int,
|
| 197 |
+
learning_rate: float,
|
| 198 |
+
beta: float,
|
| 199 |
+
rng: np.random.Generator,
|
| 200 |
+
) -> tuple[np.ndarray, float]:
|
| 201 |
+
theta = start.copy()
|
| 202 |
+
grad_norms = []
|
| 203 |
+
for _ in range(steps):
|
| 204 |
+
indexes = rng.choice(
|
| 205 |
+
len(features),
|
| 206 |
+
size=min(batch_size, len(features)),
|
| 207 |
+
replace=False,
|
| 208 |
+
)
|
| 209 |
+
gradient = batch_gradient(theta, features[indexes], beta)
|
| 210 |
+
theta -= learning_rate * gradient
|
| 211 |
+
grad_norms.append(float(gradient @ gradient))
|
| 212 |
+
return theta, float(np.mean(grad_norms))
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
def run_fed(
|
| 216 |
+
train: dict[int, np.ndarray],
|
| 217 |
+
evaluation: dict[int, np.ndarray],
|
| 218 |
+
*,
|
| 219 |
+
rounds: int,
|
| 220 |
+
local_steps: int,
|
| 221 |
+
participation: int,
|
| 222 |
+
q_max: int,
|
| 223 |
+
learning_rate: float,
|
| 224 |
+
beta: float,
|
| 225 |
+
batch_size: int,
|
| 226 |
+
seed: int,
|
| 227 |
+
) -> list[dict]:
|
| 228 |
+
rng = np.random.default_rng(seed)
|
| 229 |
+
n_clients = len(train)
|
| 230 |
+
theta = np.zeros(train[0].shape[1], dtype=np.float64)
|
| 231 |
+
history = [theta.copy()]
|
| 232 |
+
rows = []
|
| 233 |
+
for round_index in range(rounds):
|
| 234 |
+
selected = rng.choice(n_clients, size=participation, replace=False)
|
| 235 |
+
local_models = []
|
| 236 |
+
local_grad_norms = []
|
| 237 |
+
delays = []
|
| 238 |
+
for client in selected:
|
| 239 |
+
delay = int(rng.integers(0, q_max + 1)) if q_max else 0
|
| 240 |
+
delay = min(delay, len(history) - 1)
|
| 241 |
+
start = history[-1 - delay]
|
| 242 |
+
local_model, grad_norm = local_update(
|
| 243 |
+
start,
|
| 244 |
+
train[int(client)],
|
| 245 |
+
steps=local_steps,
|
| 246 |
+
batch_size=batch_size,
|
| 247 |
+
learning_rate=learning_rate,
|
| 248 |
+
beta=beta,
|
| 249 |
+
rng=rng,
|
| 250 |
+
)
|
| 251 |
+
local_models.append(local_model)
|
| 252 |
+
local_grad_norms.append(grad_norm)
|
| 253 |
+
delays.append(delay)
|
| 254 |
+
theta = np.mean(local_models, axis=0)
|
| 255 |
+
history.append(theta.copy())
|
| 256 |
+
metrics = evaluate(theta, evaluation, beta)
|
| 257 |
+
rows.append(
|
| 258 |
+
{
|
| 259 |
+
"round": round_index,
|
| 260 |
+
**metrics,
|
| 261 |
+
"train_grad_norm_sq": float(np.mean(local_grad_norms)),
|
| 262 |
+
"mean_delay": float(np.mean(delays)),
|
| 263 |
+
}
|
| 264 |
+
)
|
| 265 |
+
return rows
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
def mixing_matrix(graph_type: str, n: int) -> tuple[np.ndarray, float, float]:
|
| 269 |
+
if graph_type == "path":
|
| 270 |
+
edges = [(i, i + 1) for i in range(n - 1)]
|
| 271 |
+
elif graph_type == "ring":
|
| 272 |
+
edges = [(i, (i + 1) % n) for i in range(n)]
|
| 273 |
+
elif graph_type == "star":
|
| 274 |
+
edges = [(0, i) for i in range(1, n)]
|
| 275 |
+
elif graph_type == "complete":
|
| 276 |
+
edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
|
| 277 |
+
else:
|
| 278 |
+
raise ValueError(graph_type)
|
| 279 |
+
degree = np.zeros(n, dtype=int)
|
| 280 |
+
for i, j in edges:
|
| 281 |
+
degree[i] += 1
|
| 282 |
+
degree[j] += 1
|
| 283 |
+
pi = np.zeros((n, n), dtype=np.float64)
|
| 284 |
+
for i, j in edges:
|
| 285 |
+
weight = 1.0 / (1 + max(degree[i], degree[j]))
|
| 286 |
+
pi[i, j] = weight
|
| 287 |
+
pi[j, i] = weight
|
| 288 |
+
for i in range(n):
|
| 289 |
+
pi[i, i] = 1.0 - pi[i].sum()
|
| 290 |
+
eigenvalues = np.sort(np.abs(np.linalg.eigvalsh(pi)))[::-1]
|
| 291 |
+
rho = float(eigenvalues[1])
|
| 292 |
+
return pi, rho, 1.0 - rho**2
|
| 293 |
+
|
| 294 |
+
|
| 295 |
+
def run_decentralized(
|
| 296 |
+
train: dict[int, np.ndarray],
|
| 297 |
+
evaluation: dict[int, np.ndarray],
|
| 298 |
+
*,
|
| 299 |
+
rounds: int,
|
| 300 |
+
local_steps: int,
|
| 301 |
+
topology: str,
|
| 302 |
+
learning_rate: float,
|
| 303 |
+
beta: float,
|
| 304 |
+
batch_size: int,
|
| 305 |
+
seed: int,
|
| 306 |
+
) -> list[dict]:
|
| 307 |
+
rng = np.random.default_rng(seed)
|
| 308 |
+
n_clients = len(train)
|
| 309 |
+
dimension = train[0].shape[1]
|
| 310 |
+
agents = np.zeros((n_clients, dimension), dtype=np.float64)
|
| 311 |
+
pi, rho, spectral_gap = mixing_matrix(topology, n_clients)
|
| 312 |
+
rows = []
|
| 313 |
+
for round_index in range(rounds):
|
| 314 |
+
local_grad_norms = []
|
| 315 |
+
for client in range(n_clients):
|
| 316 |
+
agents[client], grad_norm = local_update(
|
| 317 |
+
agents[client],
|
| 318 |
+
train[client],
|
| 319 |
+
steps=local_steps,
|
| 320 |
+
batch_size=batch_size,
|
| 321 |
+
learning_rate=learning_rate,
|
| 322 |
+
beta=beta,
|
| 323 |
+
rng=rng,
|
| 324 |
+
)
|
| 325 |
+
local_grad_norms.append(grad_norm)
|
| 326 |
+
consensus_before = float(
|
| 327 |
+
np.mean(np.sum((agents - agents.mean(axis=0)) ** 2, axis=1))
|
| 328 |
+
)
|
| 329 |
+
agents = pi @ agents
|
| 330 |
+
mean_theta = agents.mean(axis=0)
|
| 331 |
+
consensus_after = float(
|
| 332 |
+
np.mean(np.sum((agents - mean_theta) ** 2, axis=1))
|
| 333 |
+
)
|
| 334 |
+
metrics = evaluate(mean_theta, evaluation, beta)
|
| 335 |
+
rows.append(
|
| 336 |
+
{
|
| 337 |
+
"round": round_index,
|
| 338 |
+
**metrics,
|
| 339 |
+
"train_grad_norm_sq": float(np.mean(local_grad_norms)),
|
| 340 |
+
"consensus_before": consensus_before,
|
| 341 |
+
"consensus_after": consensus_after,
|
| 342 |
+
"rho": rho,
|
| 343 |
+
"spectral_gap": spectral_gap,
|
| 344 |
+
}
|
| 345 |
+
)
|
| 346 |
+
return rows
|
| 347 |
+
|
| 348 |
+
|
| 349 |
+
def linear_fit(x: list[float], y: list[float]) -> dict[str, float]:
|
| 350 |
+
coefficients = np.polyfit(np.asarray(x), np.asarray(y), 1)
|
| 351 |
+
predicted = np.polyval(coefficients, x)
|
| 352 |
+
residual = float(np.sum((np.asarray(y) - predicted) ** 2))
|
| 353 |
+
total = float(np.sum((np.asarray(y) - np.mean(y)) ** 2))
|
| 354 |
+
return {
|
| 355 |
+
"slope": float(coefficients[0]),
|
| 356 |
+
"intercept": float(coefficients[1]),
|
| 357 |
+
"r_squared": 1.0 - residual / total if total > 0 else 1.0,
|
| 358 |
+
}
|
| 359 |
+
|
| 360 |
+
|
| 361 |
+
def summarize(
|
| 362 |
+
trajectories: list[dict],
|
| 363 |
+
configurations: list[dict],
|
| 364 |
+
) -> tuple[list[dict], dict]:
|
| 365 |
+
grouped: dict[tuple, list[dict]] = defaultdict(list)
|
| 366 |
+
for row in trajectories:
|
| 367 |
+
grouped[(row["study"], row["value"], row["seed"])].append(row)
|
| 368 |
+
|
| 369 |
+
final_by_config: dict[tuple, list[dict]] = defaultdict(list)
|
| 370 |
+
for (study, value, seed), rows in grouped.items():
|
| 371 |
+
final_by_config[(study, value)].append(max(rows, key=lambda row: row["round"]))
|
| 372 |
+
|
| 373 |
+
summary_rows = []
|
| 374 |
+
for config in configurations:
|
| 375 |
+
key = (config["study"], str(config["value"]))
|
| 376 |
+
finals = final_by_config[key]
|
| 377 |
+
row = {
|
| 378 |
+
"study": key[0],
|
| 379 |
+
"value": key[1],
|
| 380 |
+
"seeds": len(finals),
|
| 381 |
+
"final_grad_norm_sq_mean": float(
|
| 382 |
+
np.mean([item["grad_norm_sq"] for item in finals])
|
| 383 |
+
),
|
| 384 |
+
"final_grad_norm_sq_std": float(
|
| 385 |
+
np.std([item["grad_norm_sq"] for item in finals])
|
| 386 |
+
),
|
| 387 |
+
"final_loss_mean": float(np.mean([item["loss"] for item in finals])),
|
| 388 |
+
"final_loss_std": float(np.std([item["loss"] for item in finals])),
|
| 389 |
+
"final_consensus_mean": float(
|
| 390 |
+
np.mean([item.get("consensus_after", math.nan) for item in finals])
|
| 391 |
+
)
|
| 392 |
+
if "consensus_after" in finals[0]
|
| 393 |
+
else math.nan,
|
| 394 |
+
"spectral_gap": float(finals[0].get("spectral_gap", math.nan)),
|
| 395 |
+
}
|
| 396 |
+
summary_rows.append(row)
|
| 397 |
+
|
| 398 |
+
participation = [
|
| 399 |
+
row for row in summary_rows if row["study"] == "participation"
|
| 400 |
+
]
|
| 401 |
+
staleness = [row for row in summary_rows if row["study"] == "staleness"]
|
| 402 |
+
topology = [row for row in summary_rows if row["study"] == "topology"]
|
| 403 |
+
fits = {
|
| 404 |
+
"participation_grad_vs_inverse_S": linear_fit(
|
| 405 |
+
[1.0 / float(row["value"]) for row in participation],
|
| 406 |
+
[row["final_grad_norm_sq_mean"] for row in participation],
|
| 407 |
+
),
|
| 408 |
+
"staleness_grad_vs_q_max": linear_fit(
|
| 409 |
+
[float(row["value"]) for row in staleness],
|
| 410 |
+
[row["final_grad_norm_sq_mean"] for row in staleness],
|
| 411 |
+
),
|
| 412 |
+
"topology_consensus_vs_inverse_gap": linear_fit(
|
| 413 |
+
[1.0 / row["spectral_gap"] for row in topology],
|
| 414 |
+
[row["final_consensus_mean"] for row in topology],
|
| 415 |
+
),
|
| 416 |
+
}
|
| 417 |
+
return summary_rows, fits
|
| 418 |
+
|
| 419 |
+
|
| 420 |
+
def write_csv(path: Path, rows: list[dict]) -> None:
|
| 421 |
+
keys = sorted({key for row in rows for key in row})
|
| 422 |
+
with path.open("w", newline="", encoding="utf-8") as handle:
|
| 423 |
+
writer = csv.DictWriter(handle, fieldnames=keys)
|
| 424 |
+
writer.writeheader()
|
| 425 |
+
writer.writerows(rows)
|
| 426 |
+
|
| 427 |
+
|
| 428 |
+
def make_figure(summary_rows: list[dict], output: Path) -> None:
|
| 429 |
+
fig = make_subplots(
|
| 430 |
+
rows=2,
|
| 431 |
+
cols=2,
|
| 432 |
+
subplot_titles=(
|
| 433 |
+
"Local steps E",
|
| 434 |
+
"Participation S",
|
| 435 |
+
"Actual bounded staleness q_max",
|
| 436 |
+
"Topology and consensus",
|
| 437 |
+
),
|
| 438 |
+
)
|
| 439 |
+
panels = [
|
| 440 |
+
("local_steps", 1, 1),
|
| 441 |
+
("participation", 1, 2),
|
| 442 |
+
("staleness", 2, 1),
|
| 443 |
+
]
|
| 444 |
+
for study, row_index, col_index in panels:
|
| 445 |
+
rows = [row for row in summary_rows if row["study"] == study]
|
| 446 |
+
x = [float(row["value"]) for row in rows]
|
| 447 |
+
y = [row["final_grad_norm_sq_mean"] for row in rows]
|
| 448 |
+
error = [row["final_grad_norm_sq_std"] for row in rows]
|
| 449 |
+
fig.add_trace(
|
| 450 |
+
go.Scatter(
|
| 451 |
+
x=x,
|
| 452 |
+
y=y,
|
| 453 |
+
error_y={"type": "data", "array": error},
|
| 454 |
+
mode="lines+markers",
|
| 455 |
+
name=study,
|
| 456 |
+
),
|
| 457 |
+
row=row_index,
|
| 458 |
+
col=col_index,
|
| 459 |
+
)
|
| 460 |
+
topology_rows = [
|
| 461 |
+
row for row in summary_rows if row["study"] == "topology"
|
| 462 |
+
]
|
| 463 |
+
fig.add_trace(
|
| 464 |
+
go.Scatter(
|
| 465 |
+
x=[1.0 / row["spectral_gap"] for row in topology_rows],
|
| 466 |
+
y=[row["final_consensus_mean"] for row in topology_rows],
|
| 467 |
+
mode="markers+text",
|
| 468 |
+
text=[row["value"] for row in topology_rows],
|
| 469 |
+
textposition="top center",
|
| 470 |
+
name="topology",
|
| 471 |
+
),
|
| 472 |
+
row=2,
|
| 473 |
+
col=2,
|
| 474 |
+
)
|
| 475 |
+
fig.update_layout(
|
| 476 |
+
title="Scaled SHP log-linear DPO reproduction (N=5, mean ± std over seeds)",
|
| 477 |
+
template="plotly_white",
|
| 478 |
+
width=1200,
|
| 479 |
+
height=800,
|
| 480 |
+
)
|
| 481 |
+
fig.update_xaxes(title_text="E", row=1, col=1)
|
| 482 |
+
fig.update_xaxes(title_text="S", row=1, col=2)
|
| 483 |
+
fig.update_xaxes(title_text="q_max", row=2, col=1)
|
| 484 |
+
fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
|
| 485 |
+
fig.update_yaxes(title_text="final gradient norm²", row=1, col=1)
|
| 486 |
+
fig.update_yaxes(title_text="final gradient norm²", row=1, col=2)
|
| 487 |
+
fig.update_yaxes(title_text="final gradient norm²", row=2, col=1)
|
| 488 |
+
fig.update_yaxes(title_text="final consensus error", row=2, col=2)
|
| 489 |
+
fig.write_html(output, include_plotlyjs="cdn")
|
| 490 |
+
|
| 491 |
+
|
| 492 |
+
def main() -> None:
|
| 493 |
+
parser = argparse.ArgumentParser()
|
| 494 |
+
parser.add_argument("--output", type=Path, default=Path("empirical_outputs"))
|
| 495 |
+
parser.add_argument("--rounds", type=int, default=80)
|
| 496 |
+
parser.add_argument("--seeds", type=int, default=5)
|
| 497 |
+
parser.add_argument("--dimension", type=int, default=1024)
|
| 498 |
+
parser.add_argument("--rows-per-domain", type=int, default=160)
|
| 499 |
+
parser.add_argument("--train-rows", type=int, default=120)
|
| 500 |
+
parser.add_argument("--learning-rate", type=float, default=0.25)
|
| 501 |
+
parser.add_argument("--beta", type=float, default=0.2)
|
| 502 |
+
parser.add_argument("--batch-size", type=int, default=16)
|
| 503 |
+
args = parser.parse_args()
|
| 504 |
+
args.output.mkdir(parents=True, exist_ok=True)
|
| 505 |
+
started = time.perf_counter()
|
| 506 |
+
|
| 507 |
+
raw = load_shp(args.rows_per_domain)
|
| 508 |
+
features = featurize(raw, args.dimension)
|
| 509 |
+
train = {client: values[: args.train_rows] for client, values in features.items()}
|
| 510 |
+
evaluation = {
|
| 511 |
+
client: values[args.train_rows :] for client, values in features.items()
|
| 512 |
+
}
|
| 513 |
+
|
| 514 |
+
configurations = [
|
| 515 |
+
*[
|
| 516 |
+
{
|
| 517 |
+
"study": "local_steps",
|
| 518 |
+
"value": e,
|
| 519 |
+
"kind": "fed",
|
| 520 |
+
"local_steps": e,
|
| 521 |
+
"participation": 5,
|
| 522 |
+
"q_max": 0,
|
| 523 |
+
}
|
| 524 |
+
for e in [1, 3, 6]
|
| 525 |
+
],
|
| 526 |
+
*[
|
| 527 |
+
{
|
| 528 |
+
"study": "participation",
|
| 529 |
+
"value": s,
|
| 530 |
+
"kind": "fed",
|
| 531 |
+
"local_steps": 3,
|
| 532 |
+
"participation": s,
|
| 533 |
+
"q_max": 0,
|
| 534 |
+
}
|
| 535 |
+
for s in [1, 3, 5]
|
| 536 |
+
],
|
| 537 |
+
*[
|
| 538 |
+
{
|
| 539 |
+
"study": "staleness",
|
| 540 |
+
"value": q,
|
| 541 |
+
"kind": "fed",
|
| 542 |
+
"local_steps": 3,
|
| 543 |
+
"participation": 3,
|
| 544 |
+
"q_max": q,
|
| 545 |
+
}
|
| 546 |
+
for q in [0, 2, 5]
|
| 547 |
+
],
|
| 548 |
+
*[
|
| 549 |
+
{
|
| 550 |
+
"study": "topology",
|
| 551 |
+
"value": topology,
|
| 552 |
+
"kind": "decentralized",
|
| 553 |
+
"local_steps": 5,
|
| 554 |
+
"topology": topology,
|
| 555 |
+
}
|
| 556 |
+
for topology in ["path", "ring", "star", "complete"]
|
| 557 |
+
],
|
| 558 |
+
]
|
| 559 |
+
|
| 560 |
+
trajectories: list[dict] = []
|
| 561 |
+
for config_index, config in enumerate(configurations):
|
| 562 |
+
for seed in range(42, 42 + args.seeds):
|
| 563 |
+
if config["kind"] == "fed":
|
| 564 |
+
rows = run_fed(
|
| 565 |
+
train,
|
| 566 |
+
evaluation,
|
| 567 |
+
rounds=args.rounds,
|
| 568 |
+
local_steps=config["local_steps"],
|
| 569 |
+
participation=config["participation"],
|
| 570 |
+
q_max=config["q_max"],
|
| 571 |
+
learning_rate=args.learning_rate,
|
| 572 |
+
beta=args.beta,
|
| 573 |
+
batch_size=args.batch_size,
|
| 574 |
+
seed=seed,
|
| 575 |
+
)
|
| 576 |
+
else:
|
| 577 |
+
rows = run_decentralized(
|
| 578 |
+
train,
|
| 579 |
+
evaluation,
|
| 580 |
+
rounds=args.rounds,
|
| 581 |
+
local_steps=config["local_steps"],
|
| 582 |
+
topology=config["topology"],
|
| 583 |
+
learning_rate=args.learning_rate,
|
| 584 |
+
beta=args.beta,
|
| 585 |
+
batch_size=args.batch_size,
|
| 586 |
+
seed=seed,
|
| 587 |
+
)
|
| 588 |
+
for row in rows:
|
| 589 |
+
trajectories.append(
|
| 590 |
+
{
|
| 591 |
+
"study": config["study"],
|
| 592 |
+
"value": str(config["value"]),
|
| 593 |
+
"seed": seed,
|
| 594 |
+
**row,
|
| 595 |
+
}
|
| 596 |
+
)
|
| 597 |
+
print(
|
| 598 |
+
f"[{config_index + 1}/{len(configurations)}] "
|
| 599 |
+
f"{config['study']}={config['value']} complete"
|
| 600 |
+
)
|
| 601 |
+
|
| 602 |
+
summary_rows, fits = summarize(trajectories, configurations)
|
| 603 |
+
duration = time.perf_counter() - started
|
| 604 |
+
metadata = {
|
| 605 |
+
"scope": "scaled local proxy; real SHP, N=5, log-linear policy",
|
| 606 |
+
"full_paper_backbone": "distilgpt2 (~82M)",
|
| 607 |
+
"reproduction_policy": f"hashed log-linear ({args.dimension} dimensions)",
|
| 608 |
+
"dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 609 |
+
"dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
|
| 610 |
+
"domains": DOMAINS,
|
| 611 |
+
"n_clients": 5,
|
| 612 |
+
"train_pairs_per_client": args.train_rows,
|
| 613 |
+
"eval_pairs_per_client": args.rows_per_domain - args.train_rows,
|
| 614 |
+
"rounds": args.rounds,
|
| 615 |
+
"seeds": list(range(42, 42 + args.seeds)),
|
| 616 |
+
"learning_rate": args.learning_rate,
|
| 617 |
+
"beta": args.beta,
|
| 618 |
+
"batch_size": args.batch_size,
|
| 619 |
+
"wall_time_seconds": duration,
|
| 620 |
+
"hardware": "Apple M1 CPU, 16 GB unified memory",
|
| 621 |
+
"hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
|
| 622 |
+
"billed_cost_usd": 0.0,
|
| 623 |
+
}
|
| 624 |
+
report = {
|
| 625 |
+
"metadata": metadata,
|
| 626 |
+
"fits": fits,
|
| 627 |
+
"summary": summary_rows,
|
| 628 |
+
}
|
| 629 |
+
|
| 630 |
+
trajectories_path = args.output / "trajectories.csv"
|
| 631 |
+
summary_path = args.output / "summary.csv"
|
| 632 |
+
report_path = args.output / "results.json"
|
| 633 |
+
figure_path = args.output / "ablation_summary.html"
|
| 634 |
+
write_csv(trajectories_path, trajectories)
|
| 635 |
+
write_csv(summary_path, summary_rows)
|
| 636 |
+
report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
|
| 637 |
+
make_figure(summary_rows, figure_path)
|
| 638 |
+
|
| 639 |
+
trackio.init(
|
| 640 |
+
project="ddpo-shp-scaled-reproduction",
|
| 641 |
+
name="n5-loglinear-five-seeds",
|
| 642 |
+
config=metadata,
|
| 643 |
+
)
|
| 644 |
+
for step, row in enumerate(summary_rows):
|
| 645 |
+
trackio.log(
|
| 646 |
+
{
|
| 647 |
+
"summary/final_grad_norm_sq": row["final_grad_norm_sq_mean"],
|
| 648 |
+
"summary/final_loss": row["final_loss_mean"],
|
| 649 |
+
"summary/config_index": step,
|
| 650 |
+
},
|
| 651 |
+
step=step,
|
| 652 |
+
)
|
| 653 |
+
trackio.log(
|
| 654 |
+
{
|
| 655 |
+
"fits/participation_slope": fits[
|
| 656 |
+
"participation_grad_vs_inverse_S"
|
| 657 |
+
]["slope"],
|
| 658 |
+
"fits/staleness_slope": fits["staleness_grad_vs_q_max"]["slope"],
|
| 659 |
+
"fits/topology_slope": fits[
|
| 660 |
+
"topology_consensus_vs_inverse_gap"
|
| 661 |
+
]["slope"],
|
| 662 |
+
"wall_time_seconds": duration,
|
| 663 |
+
},
|
| 664 |
+
step=len(summary_rows),
|
| 665 |
+
)
|
| 666 |
+
trackio.log_artifact(
|
| 667 |
+
args.output,
|
| 668 |
+
name="ddpo-shp-scaled-results",
|
| 669 |
+
type="dataset",
|
| 670 |
+
aliases=["reproduction"],
|
| 671 |
+
)
|
| 672 |
+
trackio.finish()
|
| 673 |
+
|
| 674 |
+
print(json.dumps(report, indent=2))
|
| 675 |
+
|
| 676 |
+
|
| 677 |
+
if __name__ == "__main__":
|
| 678 |
+
main()
|
| 679 |
+
|
| 680 |
+
````
|
| 681 |
+
|
| 682 |
+
|
| 683 |
+
````output
|
| 684 |
+
Downloading duckdb (14.8MiB)
|
| 685 |
+
Downloaded duckdb
|
| 686 |
+
Installed 32 packages in 77ms
|
| 687 |
+
[1/13] local_steps=1 complete
|
| 688 |
+
[2/13] local_steps=3 complete
|
| 689 |
+
[3/13] local_steps=6 complete
|
| 690 |
+
[4/13] participation=1 complete
|
| 691 |
+
[5/13] participation=3 complete
|
| 692 |
+
[6/13] participation=5 complete
|
| 693 |
+
[7/13] staleness=0 complete
|
| 694 |
+
[8/13] staleness=2 complete
|
| 695 |
+
[9/13] staleness=5 complete
|
| 696 |
+
[10/13] topology=path complete
|
| 697 |
+
[11/13] topology=ring complete
|
| 698 |
+
[12/13] topology=star complete
|
| 699 |
+
[13/13] topology=complete complete
|
| 700 |
+
* Trackio project initialized: ddpo-shp-scaled-reproduction
|
| 701 |
+
* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
|
| 702 |
+
* View dashboard by running in your terminal:
|
| 703 |
+
[1m[38;5;208mtrackio show --project "ddpo-shp-scaled-reproduction"[0m
|
| 704 |
+
* or by running in Python: trackio.show(project="ddpo-shp-scaled-reproduction")
|
| 705 |
+
* Created new run: n5-loglinear-five-seeds
|
| 706 |
+
* Run finished. Uploading logs to Trackio (please wait...)
|
| 707 |
+
{
|
| 708 |
+
"metadata": {
|
| 709 |
+
"scope": "scaled local proxy; real SHP, N=5, log-linear policy",
|
| 710 |
+
"full_paper_backbone": "distilgpt2 (~82M)",
|
| 711 |
+
"reproduction_policy": "hashed log-linear (1024 dimensions)",
|
| 712 |
+
"dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 713 |
+
"dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
|
| 714 |
+
"domains": [
|
| 715 |
+
"askacademia_train",
|
| 716 |
+
"askbaking_train",
|
| 717 |
+
"askcarguys_train",
|
| 718 |
+
"askphilosophy_train",
|
| 719 |
+
"legaladvice_train"
|
| 720 |
+
],
|
| 721 |
+
"n_clients": 5,
|
| 722 |
+
"train_pairs_per_client": 120,
|
| 723 |
+
"eval_pairs_per_client": 40,
|
| 724 |
+
"rounds": 80,
|
| 725 |
+
"seeds": [
|
| 726 |
+
42,
|
| 727 |
+
43,
|
| 728 |
+
44,
|
| 729 |
+
45,
|
| 730 |
+
46
|
| 731 |
+
],
|
| 732 |
+
"learning_rate": 0.25,
|
| 733 |
+
"beta": 0.2,
|
| 734 |
+
"batch_size": 16,
|
| 735 |
+
"wall_time_seconds": 51.08550537499832,
|
| 736 |
+
"hardware": "Apple M1 CPU, 16 GB unified memory",
|
| 737 |
+
"hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
|
| 738 |
+
"billed_cost_usd": 0.0
|
| 739 |
+
},
|
| 740 |
+
"fits": {
|
| 741 |
+
"participation_grad_vs_inverse_S": {
|
| 742 |
+
"slope": -1.490254080375279e-07,
|
| 743 |
+
"intercept": 0.0002957094078217976,
|
| 744 |
+
"r_squared": 0.7302089505595288
|
| 745 |
+
},
|
| 746 |
+
"staleness_grad_vs_q_max": {
|
| 747 |
+
"slope": 6.475459349008947e-08,
|
| 748 |
+
"intercept": 0.0002957158302631729,
|
| 749 |
+
"r_squared": 0.98409493928456
|
| 750 |
+
},
|
| 751 |
+
"topology_consensus_vs_inverse_gap": {
|
| 752 |
+
"slope": 0.0015142574989828385,
|
| 753 |
+
"intercept": -0.001139720595757755,
|
| 754 |
+
"r_squared": 0.6802636259472109
|
| 755 |
+
}
|
| 756 |
+
},
|
| 757 |
+
"summary": [
|
| 758 |
+
{
|
| 759 |
+
"study": "local_steps",
|
| 760 |
+
"value": "1",
|
| 761 |
+
"seeds": 5,
|
| 762 |
+
"final_grad_norm_sq_mean": 0.0002959844413176936,
|
| 763 |
+
"final_grad_norm_sq_std": 3.1242630621229745e-08,
|
| 764 |
+
"final_loss_mean": 0.692767434637361,
|
| 765 |
+
"final_loss_std": 2.9960266660448454e-05,
|
| 766 |
+
"final_consensus_mean": NaN,
|
| 767 |
+
"spectral_gap": NaN
|
| 768 |
+
},
|
| 769 |
+
{
|
| 770 |
+
"study": "local_steps",
|
| 771 |
+
"value": "3",
|
| 772 |
+
"seeds": 5,
|
| 773 |
+
"final_grad_norm_sq_mean": 0.0002956447370211676,
|
| 774 |
+
"final_grad_norm_sq_std": 7.143137704444013e-08,
|
| 775 |
+
"final_loss_mean": 0.6921181457214483,
|
| 776 |
+
"final_loss_std": 5.369632825832004e-05,
|
| 777 |
+
"final_consensus_mean": NaN,
|
| 778 |
+
"spectral_gap": NaN
|
| 779 |
+
},
|
| 780 |
+
{
|
| 781 |
+
"study": "local_steps",
|
| 782 |
+
"value": "6",
|
| 783 |
+
"seeds": 5,
|
| 784 |
+
"final_grad_norm_sq_mean": 0.0002951760642065067,
|
| 785 |
+
"final_grad_norm_sq_std": 6.683455807540559e-08,
|
| 786 |
+
"final_loss_mean": 0.6911896374824796,
|
| 787 |
+
"final_loss_std": 3.369466591631232e-05,
|
| 788 |
+
"final_consensus_mean": NaN,
|
| 789 |
+
"spectral_gap": NaN
|
| 790 |
+
},
|
| 791 |
+
{
|
| 792 |
+
"study": "participation",
|
| 793 |
+
"value": "1",
|
| 794 |
+
"seeds": 5,
|
| 795 |
+
"final_grad_norm_sq_mean": 0.0002955534092699556,
|
| 796 |
+
"final_grad_norm_sq_std": 2.5003321107893815e-07,
|
| 797 |
+
"final_loss_mean": 0.692018887625939,
|
| 798 |
+
"final_loss_std": 0.00019644787687949202,
|
| 799 |
+
"final_consensus_mean": NaN,
|
| 800 |
+
"spectral_gap": NaN
|
| 801 |
+
},
|
| 802 |
+
{
|
| 803 |
+
"study": "participation",
|
| 804 |
+
"value": "3",
|
| 805 |
+
"seeds": 5,
|
| 806 |
+
"final_grad_norm_sq_mean": 0.00029570157154861225,
|
| 807 |
+
"final_grad_norm_sq_std": 1.2928937159579134e-07,
|
| 808 |
+
"final_loss_mean": 0.6921678162240276,
|
| 809 |
+
"final_loss_std": 7.762641835153803e-05,
|
| 810 |
+
"final_consensus_mean": NaN,
|
| 811 |
+
"spectral_gap": NaN
|
| 812 |
+
},
|
| 813 |
+
{
|
| 814 |
+
"study": "participation",
|
| 815 |
+
"value": "5",
|
| 816 |
+
"seeds": 5,
|
| 817 |
+
"final_grad_norm_sq_mean": 0.0002956447370211676,
|
| 818 |
+
"final_grad_norm_sq_std": 7.143137704444013e-08,
|
| 819 |
+
"final_loss_mean": 0.6921181457214483,
|
| 820 |
+
"final_loss_std": 5.369632825832004e-05,
|
| 821 |
+
"final_consensus_mean": NaN,
|
| 822 |
+
"spectral_gap": NaN
|
| 823 |
+
},
|
| 824 |
+
{
|
| 825 |
+
"study": "staleness",
|
| 826 |
+
"value": "0",
|
| 827 |
+
"seeds": 5,
|
| 828 |
+
"final_grad_norm_sq_mean": 0.00029570157154861225,
|
| 829 |
+
"final_grad_norm_sq_std": 1.2928937159579134e-07,
|
| 830 |
+
"final_loss_mean": 0.6921678162240276,
|
| 831 |
+
"final_loss_std": 7.762641835153803e-05,
|
| 832 |
+
"final_consensus_mean": NaN,
|
| 833 |
+
"spectral_gap": NaN
|
| 834 |
+
},
|
| 835 |
+
{
|
| 836 |
+
"study": "staleness",
|
| 837 |
+
"value": "2",
|
| 838 |
+
"seeds": 5,
|
| 839 |
+
"final_grad_norm_sq_mean": 0.0002958691039744208,
|
| 840 |
+
"final_grad_norm_sq_std": 5.3671974588500576e-08,
|
| 841 |
+
"final_loss_mean": 0.6925704904970035,
|
| 842 |
+
"final_loss_std": 3.2284360529443266e-05,
|
| 843 |
+
"final_consensus_mean": NaN,
|
| 844 |
+
"spectral_gap": NaN
|
| 845 |
+
},
|
| 846 |
+
{
|
| 847 |
+
"study": "staleness",
|
| 848 |
+
"value": "5",
|
| 849 |
+
"seeds": 5,
|
| 850 |
+
"final_grad_norm_sq_mean": 0.00029603009742091614,
|
| 851 |
+
"final_grad_norm_sq_std": 3.3972548051372546e-08,
|
| 852 |
+
"final_loss_mean": 0.6928111896109521,
|
| 853 |
+
"final_loss_std": 2.615190936965446e-05,
|
| 854 |
+
"final_consensus_mean": NaN,
|
| 855 |
+
"spectral_gap": NaN
|
| 856 |
+
},
|
| 857 |
+
{
|
| 858 |
+
"study": "topology",
|
| 859 |
+
"value": "path",
|
| 860 |
+
"seeds": 5,
|
| 861 |
+
"final_grad_norm_sq_mean": 0.0002953077578322604,
|
| 862 |
+
"final_grad_norm_sq_std": 1.1483142849219158e-07,
|
| 863 |
+
"final_loss_mean": 0.6914850457852572,
|
| 864 |
+
"final_loss_std": 0.00010632600833795026,
|
| 865 |
+
"final_consensus_mean": 0.004072685849233602,
|
| 866 |
+
"spectral_gap": 0.23843311486114604
|
| 867 |
+
},
|
| 868 |
+
{
|
| 869 |
+
"study": "topology",
|
| 870 |
+
"value": "ring",
|
| 871 |
+
"seeds": 5,
|
| 872 |
+
"final_grad_norm_sq_mean": 0.0002953075298494851,
|
| 873 |
+
"final_grad_norm_sq_std": 1.1491135702150993e-07,
|
| 874 |
+
"final_loss_mean": 0.6914844965936869,
|
| 875 |
+
"final_loss_std": 0.00010642678367370458,
|
| 876 |
+
"final_consensus_mean": 0.0003035894361151224,
|
| 877 |
+
"spectral_gap": 0.709107334583345
|
| 878 |
+
},
|
| 879 |
+
{
|
| 880 |
+
"study": "topology",
|
| 881 |
+
"value": "star",
|
| 882 |
+
"seeds": 5,
|
| 883 |
+
"final_grad_norm_sq_mean": 0.00029530756477476273,
|
| 884 |
+
"final_grad_norm_sq_std": 1.1484830376721028e-07,
|
| 885 |
+
"final_loss_mean": 0.6914853768018828,
|
| 886 |
+
"final_loss_std": 0.00010632513365832594,
|
| 887 |
+
"final_consensus_mean": 0.005271681629193685,
|
| 888 |
+
"spectral_gap": 0.3599999999999999
|
| 889 |
+
},
|
| 890 |
+
{
|
| 891 |
+
"study": "topology",
|
| 892 |
+
"value": "complete",
|
| 893 |
+
"seeds": 5,
|
| 894 |
+
"final_grad_norm_sq_mean": 0.0002953078259770435,
|
| 895 |
+
"final_grad_norm_sq_std": 1.1488566697882272e-07,
|
| 896 |
+
"final_loss_mean": 0.6914845873743779,
|
| 897 |
+
"final_loss_std": 0.00010641988295715827,
|
| 898 |
+
"final_consensus_mean": 5.8427434688283515e-33,
|
| 899 |
+
"spectral_gap": 1.0
|
| 900 |
+
}
|
| 901 |
+
]
|
| 902 |
+
}
|
| 903 |
+
|
| 904 |
+
````
|
| 905 |
+
|
| 906 |
+
|
| 907 |
+
---
|
| 908 |
+
<!-- trackio-cell
|
| 909 |
+
{"type": "artifact", "id": "cell_f350873e7c5d", "created_at": "2026-07-29T13:17:01+00:00", "title": "Artifact: trajectories.csv", "path": "empirical_outputs/trajectories.csv", "size": 708279, "artifact_type": "dataset", "auto": true}
|
| 910 |
+
-->
|
| 911 |
+
**📦 Artifact** `empirical_outputs/trajectories.csv` · dataset · 0.7 MB
|
| 912 |
+
|
| 913 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/trajectories.csv
|
| 914 |
+
|
| 915 |
+
|
| 916 |
+
---
|
| 917 |
+
<!-- trackio-cell
|
| 918 |
+
{"type": "artifact", "id": "cell_195c7a9dc98b", "created_at": "2026-07-29T13:17:01+00:00", "title": "Artifact: summary.csv", "path": "empirical_outputs/summary.csv", "size": 1700, "artifact_type": "dataset", "auto": true}
|
| 919 |
+
-->
|
| 920 |
+
**📦 Artifact** `empirical_outputs/summary.csv` · dataset · 1.7 kB
|
| 921 |
+
|
| 922 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/summary.csv
|
| 923 |
+
|
| 924 |
+
|
| 925 |
+
---
|
| 926 |
+
<!-- trackio-cell
|
| 927 |
+
{"type": "code", "id": "cell_b5b48e929423", "created_at": "2026-07-29T13:18:18+00:00", "title": "Run: uv empirical_repro.py (exit 0)", "command": ["uv", "run", "empirical_repro.py", "--output", "empirical_outputs", "--rounds", "80", "--seeds", "5", "--dimension", "1024", "--rows-per-domain", "160", "--train-rows", "120"], "exit_code": 0, "duration_s": 42.916}
|
| 928 |
+
-->
|
| 929 |
+
````bash
|
| 930 |
+
$ uv run empirical_repro.py --output empirical_outputs --rounds 80 --seeds 5 --dimension 1024 --rows-per-domain 160 --train-rows 120
|
| 931 |
+
````
|
| 932 |
+
|
| 933 |
+
exit 0 · 42.9s
|
| 934 |
+
|
| 935 |
+
|
| 936 |
+
````python title=empirical_repro.py
|
| 937 |
+
# /// script
|
| 938 |
+
# requires-python = ">=3.11"
|
| 939 |
+
# dependencies = [
|
| 940 |
+
# "duckdb>=1.3",
|
| 941 |
+
# "numpy>=2.0",
|
| 942 |
+
# "plotly>=6.0",
|
| 943 |
+
# "trackio>=0.33.0",
|
| 944 |
+
# ]
|
| 945 |
+
# ///
|
| 946 |
+
"""Scaled SHP reproduction using a log-linear DPO policy.
|
| 947 |
+
|
| 948 |
+
The paper's GPU experiment uses DistilGPT-2. Because the Hub Jobs canary is
|
| 949 |
+
blocked by account credit (HTTP 402), this local fallback uses the same N=5
|
| 950 |
+
client structure and real SHP preference pairs but a 1024-dimensional hashed
|
| 951 |
+
log-linear policy. This directly matches the policy class of the theory and
|
| 952 |
+
is intentionally labelled a scaled proxy, not a full LLM replication.
|
| 953 |
+
"""
|
| 954 |
+
|
| 955 |
+
from __future__ import annotations
|
| 956 |
+
|
| 957 |
+
import argparse
|
| 958 |
+
import csv
|
| 959 |
+
import hashlib
|
| 960 |
+
import json
|
| 961 |
+
import math
|
| 962 |
+
import random
|
| 963 |
+
import re
|
| 964 |
+
import time
|
| 965 |
+
from collections import defaultdict
|
| 966 |
+
from pathlib import Path
|
| 967 |
+
|
| 968 |
+
import duckdb
|
| 969 |
+
import numpy as np
|
| 970 |
+
import plotly.graph_objects as go
|
| 971 |
+
from plotly.subplots import make_subplots
|
| 972 |
+
import trackio
|
| 973 |
+
|
| 974 |
+
|
| 975 |
+
PARQUET_URLS = [
|
| 976 |
+
"https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
|
| 977 |
+
"refs%2Fconvert%2Fparquet/default/train/0000.parquet",
|
| 978 |
+
"https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
|
| 979 |
+
"refs%2Fconvert%2Fparquet/default/train/0001.parquet",
|
| 980 |
+
]
|
| 981 |
+
DOMAINS = [
|
| 982 |
+
"askacademia_train",
|
| 983 |
+
"askbaking_train",
|
| 984 |
+
"askcarguys_train",
|
| 985 |
+
"askphilosophy_train",
|
| 986 |
+
"legaladvice_train",
|
| 987 |
+
]
|
| 988 |
+
TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_'-]{1,}")
|
| 989 |
+
|
| 990 |
+
|
| 991 |
+
def load_shp(rows_per_domain: int) -> dict[int, list[dict]]:
|
| 992 |
+
urls = ", ".join(f"'{url}'" for url in PARQUET_URLS)
|
| 993 |
+
domains = ", ".join(f"'{domain}'" for domain in DOMAINS)
|
| 994 |
+
query = f"""
|
| 995 |
+
WITH ranked AS (
|
| 996 |
+
SELECT
|
| 997 |
+
domain,
|
| 998 |
+
human_ref_A,
|
| 999 |
+
human_ref_B,
|
| 1000 |
+
score_A,
|
| 1001 |
+
score_B,
|
| 1002 |
+
ROW_NUMBER() OVER (PARTITION BY domain ORDER BY post_id, c_root_id_A, c_root_id_B) AS rn
|
| 1003 |
+
FROM read_parquet([{urls}])
|
| 1004 |
+
WHERE domain IN ({domains}) AND score_A != score_B
|
| 1005 |
+
)
|
| 1006 |
+
SELECT domain, human_ref_A, human_ref_B, score_A, score_B
|
| 1007 |
+
FROM ranked
|
| 1008 |
+
WHERE rn <= {int(rows_per_domain)}
|
| 1009 |
+
ORDER BY domain, rn
|
| 1010 |
+
"""
|
| 1011 |
+
rows = duckdb.sql(query).fetchall()
|
| 1012 |
+
grouped: dict[str, list[dict]] = defaultdict(list)
|
| 1013 |
+
for domain, text_a, text_b, score_a, score_b in rows:
|
| 1014 |
+
if score_a > score_b:
|
| 1015 |
+
chosen, rejected = text_a, text_b
|
| 1016 |
+
else:
|
| 1017 |
+
chosen, rejected = text_b, text_a
|
| 1018 |
+
if chosen and rejected and chosen != rejected:
|
| 1019 |
+
grouped[domain].append(
|
| 1020 |
+
{"chosen": chosen[-500:], "rejected": rejected[-500:]}
|
| 1021 |
+
)
|
| 1022 |
+
missing = [domain for domain in DOMAINS if len(grouped[domain]) < rows_per_domain]
|
| 1023 |
+
if missing:
|
| 1024 |
+
raise RuntimeError(f"insufficient rows for domains: {missing}")
|
| 1025 |
+
return {index: grouped[domain] for index, domain in enumerate(DOMAINS)}
|
| 1026 |
+
|
| 1027 |
+
|
| 1028 |
+
def stable_bucket(token: str, dimension: int) -> tuple[int, float]:
|
| 1029 |
+
digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
|
| 1030 |
+
value = int.from_bytes(digest, "little")
|
| 1031 |
+
return value % dimension, 1.0 if (value >> 63) == 0 else -1.0
|
| 1032 |
+
|
| 1033 |
+
|
| 1034 |
+
def text_vector(text: str, dimension: int) -> np.ndarray:
|
| 1035 |
+
vector = np.zeros(dimension, dtype=np.float64)
|
| 1036 |
+
tokens = TOKEN_RE.findall(text.lower())
|
| 1037 |
+
for token in tokens:
|
| 1038 |
+
index, sign = stable_bucket(token, dimension)
|
| 1039 |
+
vector[index] += sign
|
| 1040 |
+
norm = np.linalg.norm(vector)
|
| 1041 |
+
if norm > 0:
|
| 1042 |
+
vector /= norm
|
| 1043 |
+
return vector
|
| 1044 |
+
|
| 1045 |
+
|
| 1046 |
+
def featurize(
|
| 1047 |
+
data: dict[int, list[dict]],
|
| 1048 |
+
dimension: int,
|
| 1049 |
+
) -> dict[int, np.ndarray]:
|
| 1050 |
+
result = {}
|
| 1051 |
+
for client, rows in data.items():
|
| 1052 |
+
result[client] = np.stack(
|
| 1053 |
+
[
|
| 1054 |
+
text_vector(row["chosen"], dimension)
|
| 1055 |
+
- text_vector(row["rejected"], dimension)
|
| 1056 |
+
for row in rows
|
| 1057 |
+
]
|
| 1058 |
+
)
|
| 1059 |
+
return result
|
| 1060 |
+
|
| 1061 |
+
|
| 1062 |
+
def sigmoid_negative(z: np.ndarray) -> np.ndarray:
|
| 1063 |
+
return np.where(
|
| 1064 |
+
z >= 0,
|
| 1065 |
+
np.exp(-z) / (1.0 + np.exp(-z)),
|
| 1066 |
+
1.0 / (1.0 + np.exp(z)),
|
| 1067 |
+
)
|
| 1068 |
+
|
| 1069 |
+
|
| 1070 |
+
def batch_gradient(
|
| 1071 |
+
theta: np.ndarray,
|
| 1072 |
+
features: np.ndarray,
|
| 1073 |
+
beta: float,
|
| 1074 |
+
) -> np.ndarray:
|
| 1075 |
+
z = beta * (features @ theta)
|
| 1076 |
+
weights = -beta * sigmoid_negative(z)
|
| 1077 |
+
return (weights[:, None] * features).mean(axis=0)
|
| 1078 |
+
|
| 1079 |
+
|
| 1080 |
+
def batch_loss(theta: np.ndarray, features: np.ndarray, beta: float) -> float:
|
| 1081 |
+
z = beta * (features @ theta)
|
| 1082 |
+
return float(np.logaddexp(0.0, -z).mean())
|
| 1083 |
+
|
| 1084 |
+
|
| 1085 |
+
def evaluate(
|
| 1086 |
+
theta: np.ndarray,
|
| 1087 |
+
eval_features: dict[int, np.ndarray],
|
| 1088 |
+
beta: float,
|
| 1089 |
+
) -> dict[str, float]:
|
| 1090 |
+
pooled = np.concatenate(list(eval_features.values()), axis=0)
|
| 1091 |
+
global_gradient = batch_gradient(theta, pooled, beta)
|
| 1092 |
+
client_gradients = np.stack(
|
| 1093 |
+
[batch_gradient(theta, rows, beta) for rows in eval_features.values()]
|
| 1094 |
+
)
|
| 1095 |
+
heterogeneity = float(
|
| 1096 |
+
np.mean(np.sum((client_gradients - global_gradient) ** 2, axis=1))
|
| 1097 |
+
)
|
| 1098 |
+
return {
|
| 1099 |
+
"loss": batch_loss(theta, pooled, beta),
|
| 1100 |
+
"grad_norm_sq": float(global_gradient @ global_gradient),
|
| 1101 |
+
"kappa_sq_proxy": heterogeneity,
|
| 1102 |
+
}
|
| 1103 |
+
|
| 1104 |
+
|
| 1105 |
+
def local_update(
|
| 1106 |
+
start: np.ndarray,
|
| 1107 |
+
features: np.ndarray,
|
| 1108 |
+
*,
|
| 1109 |
+
steps: int,
|
| 1110 |
+
batch_size: int,
|
| 1111 |
+
learning_rate: float,
|
| 1112 |
+
beta: float,
|
| 1113 |
+
rng: np.random.Generator,
|
| 1114 |
+
) -> tuple[np.ndarray, float]:
|
| 1115 |
+
theta = start.copy()
|
| 1116 |
+
grad_norms = []
|
| 1117 |
+
for _ in range(steps):
|
| 1118 |
+
indexes = rng.choice(
|
| 1119 |
+
len(features),
|
| 1120 |
+
size=min(batch_size, len(features)),
|
| 1121 |
+
replace=False,
|
| 1122 |
+
)
|
| 1123 |
+
gradient = batch_gradient(theta, features[indexes], beta)
|
| 1124 |
+
theta -= learning_rate * gradient
|
| 1125 |
+
grad_norms.append(float(gradient @ gradient))
|
| 1126 |
+
return theta, float(np.mean(grad_norms))
|
| 1127 |
+
|
| 1128 |
+
|
| 1129 |
+
def run_fed(
|
| 1130 |
+
train: dict[int, np.ndarray],
|
| 1131 |
+
evaluation: dict[int, np.ndarray],
|
| 1132 |
+
*,
|
| 1133 |
+
rounds: int,
|
| 1134 |
+
local_steps: int,
|
| 1135 |
+
participation: int,
|
| 1136 |
+
q_max: int,
|
| 1137 |
+
learning_rate: float,
|
| 1138 |
+
beta: float,
|
| 1139 |
+
batch_size: int,
|
| 1140 |
+
seed: int,
|
| 1141 |
+
) -> list[dict]:
|
| 1142 |
+
rng = np.random.default_rng(seed)
|
| 1143 |
+
n_clients = len(train)
|
| 1144 |
+
theta = np.zeros(train[0].shape[1], dtype=np.float64)
|
| 1145 |
+
history = [theta.copy()]
|
| 1146 |
+
rows = []
|
| 1147 |
+
for round_index in range(rounds):
|
| 1148 |
+
selected = rng.choice(n_clients, size=participation, replace=False)
|
| 1149 |
+
local_models = []
|
| 1150 |
+
local_grad_norms = []
|
| 1151 |
+
delays = []
|
| 1152 |
+
for client in selected:
|
| 1153 |
+
delay = int(rng.integers(0, q_max + 1)) if q_max else 0
|
| 1154 |
+
delay = min(delay, len(history) - 1)
|
| 1155 |
+
start = history[-1 - delay]
|
| 1156 |
+
local_model, grad_norm = local_update(
|
| 1157 |
+
start,
|
| 1158 |
+
train[int(client)],
|
| 1159 |
+
steps=local_steps,
|
| 1160 |
+
batch_size=batch_size,
|
| 1161 |
+
learning_rate=learning_rate,
|
| 1162 |
+
beta=beta,
|
| 1163 |
+
rng=rng,
|
| 1164 |
+
)
|
| 1165 |
+
local_models.append(local_model)
|
| 1166 |
+
local_grad_norms.append(grad_norm)
|
| 1167 |
+
delays.append(delay)
|
| 1168 |
+
theta = np.mean(local_models, axis=0)
|
| 1169 |
+
history.append(theta.copy())
|
| 1170 |
+
metrics = evaluate(theta, evaluation, beta)
|
| 1171 |
+
rows.append(
|
| 1172 |
+
{
|
| 1173 |
+
"round": round_index,
|
| 1174 |
+
**metrics,
|
| 1175 |
+
"train_grad_norm_sq": float(np.mean(local_grad_norms)),
|
| 1176 |
+
"mean_delay": float(np.mean(delays)),
|
| 1177 |
+
}
|
| 1178 |
+
)
|
| 1179 |
+
return rows
|
| 1180 |
+
|
| 1181 |
+
|
| 1182 |
+
def mixing_matrix(graph_type: str, n: int) -> tuple[np.ndarray, float, float]:
|
| 1183 |
+
if graph_type == "path":
|
| 1184 |
+
edges = [(i, i + 1) for i in range(n - 1)]
|
| 1185 |
+
elif graph_type == "ring":
|
| 1186 |
+
edges = [(i, (i + 1) % n) for i in range(n)]
|
| 1187 |
+
elif graph_type == "star":
|
| 1188 |
+
edges = [(0, i) for i in range(1, n)]
|
| 1189 |
+
elif graph_type == "complete":
|
| 1190 |
+
edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
|
| 1191 |
+
else:
|
| 1192 |
+
raise ValueError(graph_type)
|
| 1193 |
+
degree = np.zeros(n, dtype=int)
|
| 1194 |
+
for i, j in edges:
|
| 1195 |
+
degree[i] += 1
|
| 1196 |
+
degree[j] += 1
|
| 1197 |
+
pi = np.zeros((n, n), dtype=np.float64)
|
| 1198 |
+
for i, j in edges:
|
| 1199 |
+
weight = 1.0 / (1 + max(degree[i], degree[j]))
|
| 1200 |
+
pi[i, j] = weight
|
| 1201 |
+
pi[j, i] = weight
|
| 1202 |
+
for i in range(n):
|
| 1203 |
+
pi[i, i] = 1.0 - pi[i].sum()
|
| 1204 |
+
eigenvalues = np.sort(np.abs(np.linalg.eigvalsh(pi)))[::-1]
|
| 1205 |
+
rho = float(eigenvalues[1])
|
| 1206 |
+
return pi, rho, 1.0 - rho**2
|
| 1207 |
+
|
| 1208 |
+
|
| 1209 |
+
def run_decentralized(
|
| 1210 |
+
train: dict[int, np.ndarray],
|
| 1211 |
+
evaluation: dict[int, np.ndarray],
|
| 1212 |
+
*,
|
| 1213 |
+
rounds: int,
|
| 1214 |
+
local_steps: int,
|
| 1215 |
+
topology: str,
|
| 1216 |
+
learning_rate: float,
|
| 1217 |
+
beta: float,
|
| 1218 |
+
batch_size: int,
|
| 1219 |
+
seed: int,
|
| 1220 |
+
) -> list[dict]:
|
| 1221 |
+
rng = np.random.default_rng(seed)
|
| 1222 |
+
n_clients = len(train)
|
| 1223 |
+
dimension = train[0].shape[1]
|
| 1224 |
+
agents = np.zeros((n_clients, dimension), dtype=np.float64)
|
| 1225 |
+
pi, rho, spectral_gap = mixing_matrix(topology, n_clients)
|
| 1226 |
+
rows = []
|
| 1227 |
+
for round_index in range(rounds):
|
| 1228 |
+
local_grad_norms = []
|
| 1229 |
+
for client in range(n_clients):
|
| 1230 |
+
agents[client], grad_norm = local_update(
|
| 1231 |
+
agents[client],
|
| 1232 |
+
train[client],
|
| 1233 |
+
steps=local_steps,
|
| 1234 |
+
batch_size=batch_size,
|
| 1235 |
+
learning_rate=learning_rate,
|
| 1236 |
+
beta=beta,
|
| 1237 |
+
rng=rng,
|
| 1238 |
+
)
|
| 1239 |
+
local_grad_norms.append(grad_norm)
|
| 1240 |
+
consensus_before = float(
|
| 1241 |
+
np.mean(np.sum((agents - agents.mean(axis=0)) ** 2, axis=1))
|
| 1242 |
+
)
|
| 1243 |
+
agents = pi @ agents
|
| 1244 |
+
mean_theta = agents.mean(axis=0)
|
| 1245 |
+
consensus_after = float(
|
| 1246 |
+
np.mean(np.sum((agents - mean_theta) ** 2, axis=1))
|
| 1247 |
+
)
|
| 1248 |
+
metrics = evaluate(mean_theta, evaluation, beta)
|
| 1249 |
+
rows.append(
|
| 1250 |
+
{
|
| 1251 |
+
"round": round_index,
|
| 1252 |
+
**metrics,
|
| 1253 |
+
"train_grad_norm_sq": float(np.mean(local_grad_norms)),
|
| 1254 |
+
"consensus_before": consensus_before,
|
| 1255 |
+
"consensus_after": consensus_after,
|
| 1256 |
+
"rho": rho,
|
| 1257 |
+
"spectral_gap": spectral_gap,
|
| 1258 |
+
}
|
| 1259 |
+
)
|
| 1260 |
+
return rows
|
| 1261 |
+
|
| 1262 |
+
|
| 1263 |
+
def linear_fit(x: list[float], y: list[float]) -> dict[str, float]:
|
| 1264 |
+
coefficients = np.polyfit(np.asarray(x), np.asarray(y), 1)
|
| 1265 |
+
predicted = np.polyval(coefficients, x)
|
| 1266 |
+
residual = float(np.sum((np.asarray(y) - predicted) ** 2))
|
| 1267 |
+
total = float(np.sum((np.asarray(y) - np.mean(y)) ** 2))
|
| 1268 |
+
return {
|
| 1269 |
+
"slope": float(coefficients[0]),
|
| 1270 |
+
"intercept": float(coefficients[1]),
|
| 1271 |
+
"r_squared": 1.0 - residual / total if total > 0 else 1.0,
|
| 1272 |
+
}
|
| 1273 |
+
|
| 1274 |
+
|
| 1275 |
+
def summarize(
|
| 1276 |
+
trajectories: list[dict],
|
| 1277 |
+
configurations: list[dict],
|
| 1278 |
+
) -> tuple[list[dict], dict]:
|
| 1279 |
+
grouped: dict[tuple, list[dict]] = defaultdict(list)
|
| 1280 |
+
for row in trajectories:
|
| 1281 |
+
grouped[(row["study"], row["value"], row["seed"])].append(row)
|
| 1282 |
+
|
| 1283 |
+
final_by_config: dict[tuple, list[dict]] = defaultdict(list)
|
| 1284 |
+
for (study, value, seed), rows in grouped.items():
|
| 1285 |
+
final_by_config[(study, value)].append(max(rows, key=lambda row: row["round"]))
|
| 1286 |
+
|
| 1287 |
+
summary_rows = []
|
| 1288 |
+
for config in configurations:
|
| 1289 |
+
key = (config["study"], str(config["value"]))
|
| 1290 |
+
finals = final_by_config[key]
|
| 1291 |
+
row = {
|
| 1292 |
+
"study": key[0],
|
| 1293 |
+
"value": key[1],
|
| 1294 |
+
"seeds": len(finals),
|
| 1295 |
+
"final_grad_norm_sq_mean": float(
|
| 1296 |
+
np.mean([item["grad_norm_sq"] for item in finals])
|
| 1297 |
+
),
|
| 1298 |
+
"final_grad_norm_sq_std": float(
|
| 1299 |
+
np.std([item["grad_norm_sq"] for item in finals])
|
| 1300 |
+
),
|
| 1301 |
+
"final_loss_mean": float(np.mean([item["loss"] for item in finals])),
|
| 1302 |
+
"final_loss_std": float(np.std([item["loss"] for item in finals])),
|
| 1303 |
+
"final_consensus_mean": float(
|
| 1304 |
+
np.mean([item.get("consensus_after", math.nan) for item in finals])
|
| 1305 |
+
)
|
| 1306 |
+
if "consensus_after" in finals[0]
|
| 1307 |
+
else math.nan,
|
| 1308 |
+
"spectral_gap": float(finals[0].get("spectral_gap", math.nan)),
|
| 1309 |
+
}
|
| 1310 |
+
summary_rows.append(row)
|
| 1311 |
+
|
| 1312 |
+
participation = [
|
| 1313 |
+
row for row in summary_rows if row["study"] == "participation"
|
| 1314 |
+
]
|
| 1315 |
+
staleness = [row for row in summary_rows if row["study"] == "staleness"]
|
| 1316 |
+
topology = [row for row in summary_rows if row["study"] == "topology"]
|
| 1317 |
+
fits = {
|
| 1318 |
+
"participation_grad_vs_inverse_S": linear_fit(
|
| 1319 |
+
[1.0 / float(row["value"]) for row in participation],
|
| 1320 |
+
[row["final_grad_norm_sq_mean"] for row in participation],
|
| 1321 |
+
),
|
| 1322 |
+
"participation_seed_std_vs_inverse_S": linear_fit(
|
| 1323 |
+
[1.0 / float(row["value"]) for row in participation],
|
| 1324 |
+
[row["final_grad_norm_sq_std"] for row in participation],
|
| 1325 |
+
),
|
| 1326 |
+
"staleness_grad_vs_q_max": linear_fit(
|
| 1327 |
+
[float(row["value"]) for row in staleness],
|
| 1328 |
+
[row["final_grad_norm_sq_mean"] for row in staleness],
|
| 1329 |
+
),
|
| 1330 |
+
"topology_consensus_vs_inverse_gap": linear_fit(
|
| 1331 |
+
[1.0 / row["spectral_gap"] for row in topology],
|
| 1332 |
+
[row["final_consensus_mean"] for row in topology],
|
| 1333 |
+
),
|
| 1334 |
+
}
|
| 1335 |
+
return summary_rows, fits
|
| 1336 |
+
|
| 1337 |
+
|
| 1338 |
+
def write_csv(path: Path, rows: list[dict]) -> None:
|
| 1339 |
+
keys = sorted({key for row in rows for key in row})
|
| 1340 |
+
with path.open("w", newline="", encoding="utf-8") as handle:
|
| 1341 |
+
writer = csv.DictWriter(handle, fieldnames=keys)
|
| 1342 |
+
writer.writeheader()
|
| 1343 |
+
writer.writerows(rows)
|
| 1344 |
+
|
| 1345 |
+
|
| 1346 |
+
def make_figure(summary_rows: list[dict], output: Path) -> None:
|
| 1347 |
+
fig = make_subplots(
|
| 1348 |
+
rows=2,
|
| 1349 |
+
cols=2,
|
| 1350 |
+
subplot_titles=(
|
| 1351 |
+
"Local steps E",
|
| 1352 |
+
"Participation S",
|
| 1353 |
+
"Actual bounded staleness q_max",
|
| 1354 |
+
"Topology and consensus",
|
| 1355 |
+
),
|
| 1356 |
+
)
|
| 1357 |
+
panels = [
|
| 1358 |
+
("local_steps", 1, 1),
|
| 1359 |
+
("participation", 1, 2),
|
| 1360 |
+
("staleness", 2, 1),
|
| 1361 |
+
]
|
| 1362 |
+
for study, row_index, col_index in panels:
|
| 1363 |
+
rows = [row for row in summary_rows if row["study"] == study]
|
| 1364 |
+
x = [float(row["value"]) for row in rows]
|
| 1365 |
+
y = [row["final_grad_norm_sq_mean"] for row in rows]
|
| 1366 |
+
error = [row["final_grad_norm_sq_std"] for row in rows]
|
| 1367 |
+
fig.add_trace(
|
| 1368 |
+
go.Scatter(
|
| 1369 |
+
x=x,
|
| 1370 |
+
y=y,
|
| 1371 |
+
error_y={"type": "data", "array": error},
|
| 1372 |
+
mode="lines+markers",
|
| 1373 |
+
name=study,
|
| 1374 |
+
),
|
| 1375 |
+
row=row_index,
|
| 1376 |
+
col=col_index,
|
| 1377 |
+
)
|
| 1378 |
+
topology_rows = [
|
| 1379 |
+
row for row in summary_rows if row["study"] == "topology"
|
| 1380 |
+
]
|
| 1381 |
+
fig.add_trace(
|
| 1382 |
+
go.Scatter(
|
| 1383 |
+
x=[1.0 / row["spectral_gap"] for row in topology_rows],
|
| 1384 |
+
y=[row["final_consensus_mean"] for row in topology_rows],
|
| 1385 |
+
mode="markers+text",
|
| 1386 |
+
text=[row["value"] for row in topology_rows],
|
| 1387 |
+
textposition="top center",
|
| 1388 |
+
name="topology",
|
| 1389 |
+
),
|
| 1390 |
+
row=2,
|
| 1391 |
+
col=2,
|
| 1392 |
+
)
|
| 1393 |
+
fig.update_layout(
|
| 1394 |
+
title="Scaled SHP log-linear DPO reproduction (N=5, mean ± std over seeds)",
|
| 1395 |
+
template="plotly_white",
|
| 1396 |
+
width=1200,
|
| 1397 |
+
height=800,
|
| 1398 |
+
)
|
| 1399 |
+
fig.update_xaxes(title_text="E", row=1, col=1)
|
| 1400 |
+
fig.update_xaxes(title_text="S", row=1, col=2)
|
| 1401 |
+
fig.update_xaxes(title_text="q_max", row=2, col=1)
|
| 1402 |
+
fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
|
| 1403 |
+
fig.update_yaxes(title_text="final gradient norm²", row=1, col=1)
|
| 1404 |
+
fig.update_yaxes(title_text="final gradient norm²", row=1, col=2)
|
| 1405 |
+
fig.update_yaxes(title_text="final gradient norm²", row=2, col=1)
|
| 1406 |
+
fig.update_yaxes(title_text="final consensus error", row=2, col=2)
|
| 1407 |
+
fig.write_html(output, include_plotlyjs="cdn")
|
| 1408 |
+
|
| 1409 |
+
|
| 1410 |
+
def main() -> None:
|
| 1411 |
+
parser = argparse.ArgumentParser()
|
| 1412 |
+
parser.add_argument("--output", type=Path, default=Path("empirical_outputs"))
|
| 1413 |
+
parser.add_argument("--rounds", type=int, default=80)
|
| 1414 |
+
parser.add_argument("--seeds", type=int, default=5)
|
| 1415 |
+
parser.add_argument("--dimension", type=int, default=1024)
|
| 1416 |
+
parser.add_argument("--rows-per-domain", type=int, default=160)
|
| 1417 |
+
parser.add_argument("--train-rows", type=int, default=120)
|
| 1418 |
+
parser.add_argument("--learning-rate", type=float, default=0.25)
|
| 1419 |
+
parser.add_argument("--beta", type=float, default=0.2)
|
| 1420 |
+
parser.add_argument("--batch-size", type=int, default=16)
|
| 1421 |
+
args = parser.parse_args()
|
| 1422 |
+
args.output.mkdir(parents=True, exist_ok=True)
|
| 1423 |
+
started = time.perf_counter()
|
| 1424 |
+
|
| 1425 |
+
raw = load_shp(args.rows_per_domain)
|
| 1426 |
+
features = featurize(raw, args.dimension)
|
| 1427 |
+
train = {client: values[: args.train_rows] for client, values in features.items()}
|
| 1428 |
+
evaluation = {
|
| 1429 |
+
client: values[args.train_rows :] for client, values in features.items()
|
| 1430 |
+
}
|
| 1431 |
+
|
| 1432 |
+
configurations = [
|
| 1433 |
+
*[
|
| 1434 |
+
{
|
| 1435 |
+
"study": "local_steps",
|
| 1436 |
+
"value": e,
|
| 1437 |
+
"kind": "fed",
|
| 1438 |
+
"local_steps": e,
|
| 1439 |
+
"participation": 5,
|
| 1440 |
+
"q_max": 0,
|
| 1441 |
+
}
|
| 1442 |
+
for e in [1, 3, 6]
|
| 1443 |
+
],
|
| 1444 |
+
*[
|
| 1445 |
+
{
|
| 1446 |
+
"study": "participation",
|
| 1447 |
+
"value": s,
|
| 1448 |
+
"kind": "fed",
|
| 1449 |
+
"local_steps": 3,
|
| 1450 |
+
"participation": s,
|
| 1451 |
+
"q_max": 0,
|
| 1452 |
+
}
|
| 1453 |
+
for s in [1, 3, 5]
|
| 1454 |
+
],
|
| 1455 |
+
*[
|
| 1456 |
+
{
|
| 1457 |
+
"study": "staleness",
|
| 1458 |
+
"value": q,
|
| 1459 |
+
"kind": "fed",
|
| 1460 |
+
"local_steps": 3,
|
| 1461 |
+
"participation": 3,
|
| 1462 |
+
"q_max": q,
|
| 1463 |
+
}
|
| 1464 |
+
for q in [0, 2, 5]
|
| 1465 |
+
],
|
| 1466 |
+
*[
|
| 1467 |
+
{
|
| 1468 |
+
"study": "topology",
|
| 1469 |
+
"value": topology,
|
| 1470 |
+
"kind": "decentralized",
|
| 1471 |
+
"local_steps": 5,
|
| 1472 |
+
"topology": topology,
|
| 1473 |
+
}
|
| 1474 |
+
for topology in ["path", "ring", "star", "complete"]
|
| 1475 |
+
],
|
| 1476 |
+
]
|
| 1477 |
+
|
| 1478 |
+
trajectories: list[dict] = []
|
| 1479 |
+
for config_index, config in enumerate(configurations):
|
| 1480 |
+
for seed in range(42, 42 + args.seeds):
|
| 1481 |
+
if config["kind"] == "fed":
|
| 1482 |
+
rows = run_fed(
|
| 1483 |
+
train,
|
| 1484 |
+
evaluation,
|
| 1485 |
+
rounds=args.rounds,
|
| 1486 |
+
local_steps=config["local_steps"],
|
| 1487 |
+
participation=config["participation"],
|
| 1488 |
+
q_max=config["q_max"],
|
| 1489 |
+
learning_rate=args.learning_rate,
|
| 1490 |
+
beta=args.beta,
|
| 1491 |
+
batch_size=args.batch_size,
|
| 1492 |
+
seed=seed,
|
| 1493 |
+
)
|
| 1494 |
+
else:
|
| 1495 |
+
rows = run_decentralized(
|
| 1496 |
+
train,
|
| 1497 |
+
evaluation,
|
| 1498 |
+
rounds=args.rounds,
|
| 1499 |
+
local_steps=config["local_steps"],
|
| 1500 |
+
topology=config["topology"],
|
| 1501 |
+
learning_rate=args.learning_rate,
|
| 1502 |
+
beta=args.beta,
|
| 1503 |
+
batch_size=args.batch_size,
|
| 1504 |
+
seed=seed,
|
| 1505 |
+
)
|
| 1506 |
+
for row in rows:
|
| 1507 |
+
trajectories.append(
|
| 1508 |
+
{
|
| 1509 |
+
"study": config["study"],
|
| 1510 |
+
"value": str(config["value"]),
|
| 1511 |
+
"seed": seed,
|
| 1512 |
+
**row,
|
| 1513 |
+
}
|
| 1514 |
+
)
|
| 1515 |
+
print(
|
| 1516 |
+
f"[{config_index + 1}/{len(configurations)}] "
|
| 1517 |
+
f"{config['study']}={config['value']} complete"
|
| 1518 |
+
)
|
| 1519 |
+
|
| 1520 |
+
summary_rows, fits = summarize(trajectories, configurations)
|
| 1521 |
+
duration = time.perf_counter() - started
|
| 1522 |
+
metadata = {
|
| 1523 |
+
"scope": "scaled local proxy; real SHP, N=5, log-linear policy",
|
| 1524 |
+
"full_paper_backbone": "distilgpt2 (~82M)",
|
| 1525 |
+
"reproduction_policy": f"hashed log-linear ({args.dimension} dimensions)",
|
| 1526 |
+
"dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 1527 |
+
"dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
|
| 1528 |
+
"domains": DOMAINS,
|
| 1529 |
+
"n_clients": 5,
|
| 1530 |
+
"train_pairs_per_client": args.train_rows,
|
| 1531 |
+
"eval_pairs_per_client": args.rows_per_domain - args.train_rows,
|
| 1532 |
+
"rounds": args.rounds,
|
| 1533 |
+
"seeds": list(range(42, 42 + args.seeds)),
|
| 1534 |
+
"learning_rate": args.learning_rate,
|
| 1535 |
+
"beta": args.beta,
|
| 1536 |
+
"batch_size": args.batch_size,
|
| 1537 |
+
"wall_time_seconds": duration,
|
| 1538 |
+
"hardware": "Apple M1 CPU, 16 GB unified memory",
|
| 1539 |
+
"hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
|
| 1540 |
+
"billed_cost_usd": 0.0,
|
| 1541 |
+
}
|
| 1542 |
+
report = {
|
| 1543 |
+
"metadata": metadata,
|
| 1544 |
+
"fits": fits,
|
| 1545 |
+
"summary": summary_rows,
|
| 1546 |
+
}
|
| 1547 |
+
|
| 1548 |
+
trajectories_path = args.output / "trajectories.csv"
|
| 1549 |
+
summary_path = args.output / "summary.csv"
|
| 1550 |
+
report_path = args.output / "results.json"
|
| 1551 |
+
figure_path = args.output / "ablation_summary.html"
|
| 1552 |
+
write_csv(trajectories_path, trajectories)
|
| 1553 |
+
write_csv(summary_path, summary_rows)
|
| 1554 |
+
report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
|
| 1555 |
+
make_figure(summary_rows, figure_path)
|
| 1556 |
+
|
| 1557 |
+
trackio.init(
|
| 1558 |
+
project="ddpo-shp-scaled-reproduction",
|
| 1559 |
+
name="n5-loglinear-five-seeds",
|
| 1560 |
+
config=metadata,
|
| 1561 |
+
)
|
| 1562 |
+
for step, row in enumerate(summary_rows):
|
| 1563 |
+
trackio.log(
|
| 1564 |
+
{
|
| 1565 |
+
"summary/final_grad_norm_sq": row["final_grad_norm_sq_mean"],
|
| 1566 |
+
"summary/final_loss": row["final_loss_mean"],
|
| 1567 |
+
"summary/config_index": step,
|
| 1568 |
+
},
|
| 1569 |
+
step=step,
|
| 1570 |
+
)
|
| 1571 |
+
trackio.log(
|
| 1572 |
+
{
|
| 1573 |
+
"fits/participation_slope": fits[
|
| 1574 |
+
"participation_grad_vs_inverse_S"
|
| 1575 |
+
]["slope"],
|
| 1576 |
+
"fits/participation_seed_std_slope": fits[
|
| 1577 |
+
"participation_seed_std_vs_inverse_S"
|
| 1578 |
+
]["slope"],
|
| 1579 |
+
"fits/staleness_slope": fits["staleness_grad_vs_q_max"]["slope"],
|
| 1580 |
+
"fits/topology_slope": fits[
|
| 1581 |
+
"topology_consensus_vs_inverse_gap"
|
| 1582 |
+
]["slope"],
|
| 1583 |
+
"wall_time_seconds": duration,
|
| 1584 |
+
},
|
| 1585 |
+
step=len(summary_rows),
|
| 1586 |
+
)
|
| 1587 |
+
trackio.log_artifact(
|
| 1588 |
+
args.output,
|
| 1589 |
+
name="ddpo-shp-scaled-results",
|
| 1590 |
+
type="dataset",
|
| 1591 |
+
aliases=["reproduction"],
|
| 1592 |
+
)
|
| 1593 |
+
trackio.finish()
|
| 1594 |
+
|
| 1595 |
+
print(json.dumps(report, indent=2))
|
| 1596 |
+
|
| 1597 |
+
|
| 1598 |
+
if __name__ == "__main__":
|
| 1599 |
+
main()
|
| 1600 |
+
|
| 1601 |
+
````
|
| 1602 |
+
|
| 1603 |
+
|
| 1604 |
+
````output
|
| 1605 |
+
[1/13] local_steps=1 complete
|
| 1606 |
+
[2/13] local_steps=3 complete
|
| 1607 |
+
[3/13] local_steps=6 complete
|
| 1608 |
+
[4/13] participation=1 complete
|
| 1609 |
+
[5/13] participation=3 complete
|
| 1610 |
+
[6/13] participation=5 complete
|
| 1611 |
+
[7/13] staleness=0 complete
|
| 1612 |
+
[8/13] staleness=2 complete
|
| 1613 |
+
[9/13] staleness=5 complete
|
| 1614 |
+
[10/13] topology=path complete
|
| 1615 |
+
[11/13] topology=ring complete
|
| 1616 |
+
[12/13] topology=star complete
|
| 1617 |
+
[13/13] topology=complete complete
|
| 1618 |
+
* Trackio project initialized: ddpo-shp-scaled-reproduction
|
| 1619 |
+
* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
|
| 1620 |
+
* View dashboard by running in your terminal:
|
| 1621 |
+
[1m[38;5;208mtrackio show --project "ddpo-shp-scaled-reproduction"[0m
|
| 1622 |
+
* or by running in Python: trackio.show(project="ddpo-shp-scaled-reproduction")
|
| 1623 |
+
* Created new run: n5-loglinear-five-seeds
|
| 1624 |
+
* Run finished. Uploading logs to Trackio (please wait...)
|
| 1625 |
+
{
|
| 1626 |
+
"metadata": {
|
| 1627 |
+
"scope": "scaled local proxy; real SHP, N=5, log-linear policy",
|
| 1628 |
+
"full_paper_backbone": "distilgpt2 (~82M)",
|
| 1629 |
+
"reproduction_policy": "hashed log-linear (1024 dimensions)",
|
| 1630 |
+
"dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 1631 |
+
"dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
|
| 1632 |
+
"domains": [
|
| 1633 |
+
"askacademia_train",
|
| 1634 |
+
"askbaking_train",
|
| 1635 |
+
"askcarguys_train",
|
| 1636 |
+
"askphilosophy_train",
|
| 1637 |
+
"legaladvice_train"
|
| 1638 |
+
],
|
| 1639 |
+
"n_clients": 5,
|
| 1640 |
+
"train_pairs_per_client": 120,
|
| 1641 |
+
"eval_pairs_per_client": 40,
|
| 1642 |
+
"rounds": 80,
|
| 1643 |
+
"seeds": [
|
| 1644 |
+
42,
|
| 1645 |
+
43,
|
| 1646 |
+
44,
|
| 1647 |
+
45,
|
| 1648 |
+
46
|
| 1649 |
+
],
|
| 1650 |
+
"learning_rate": 0.25,
|
| 1651 |
+
"beta": 0.2,
|
| 1652 |
+
"batch_size": 16,
|
| 1653 |
+
"wall_time_seconds": 41.70772958299494,
|
| 1654 |
+
"hardware": "Apple M1 CPU, 16 GB unified memory",
|
| 1655 |
+
"hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
|
| 1656 |
+
"billed_cost_usd": 0.0
|
| 1657 |
+
},
|
| 1658 |
+
"fits": {
|
| 1659 |
+
"participation_grad_vs_inverse_S": {
|
| 1660 |
+
"slope": -1.490254080375279e-07,
|
| 1661 |
+
"intercept": 0.0002957094078217976,
|
| 1662 |
+
"r_squared": 0.7302089505595288
|
| 1663 |
+
},
|
| 1664 |
+
"participation_seed_std_vs_inverse_S": {
|
| 1665 |
+
"slope": 2.0965986244041204e-07,
|
| 1666 |
+
"intercept": 4.309183465906812e-08,
|
| 1667 |
+
"r_squared": 0.9724121551177207
|
| 1668 |
+
},
|
| 1669 |
+
"staleness_grad_vs_q_max": {
|
| 1670 |
+
"slope": 6.475459349008947e-08,
|
| 1671 |
+
"intercept": 0.0002957158302631729,
|
| 1672 |
+
"r_squared": 0.98409493928456
|
| 1673 |
+
},
|
| 1674 |
+
"topology_consensus_vs_inverse_gap": {
|
| 1675 |
+
"slope": 0.0015142574989828385,
|
| 1676 |
+
"intercept": -0.001139720595757755,
|
| 1677 |
+
"r_squared": 0.6802636259472109
|
| 1678 |
+
}
|
| 1679 |
+
},
|
| 1680 |
+
"summary": [
|
| 1681 |
+
{
|
| 1682 |
+
"study": "local_steps",
|
| 1683 |
+
"value": "1",
|
| 1684 |
+
"seeds": 5,
|
| 1685 |
+
"final_grad_norm_sq_mean": 0.0002959844413176936,
|
| 1686 |
+
"final_grad_norm_sq_std": 3.1242630621229745e-08,
|
| 1687 |
+
"final_loss_mean": 0.692767434637361,
|
| 1688 |
+
"final_loss_std": 2.9960266660448454e-05,
|
| 1689 |
+
"final_consensus_mean": NaN,
|
| 1690 |
+
"spectral_gap": NaN
|
| 1691 |
+
},
|
| 1692 |
+
{
|
| 1693 |
+
"study": "local_steps",
|
| 1694 |
+
"value": "3",
|
| 1695 |
+
"seeds": 5,
|
| 1696 |
+
"final_grad_norm_sq_mean": 0.0002956447370211676,
|
| 1697 |
+
"final_grad_norm_sq_std": 7.143137704444013e-08,
|
| 1698 |
+
"final_loss_mean": 0.6921181457214483,
|
| 1699 |
+
"final_loss_std": 5.369632825832004e-05,
|
| 1700 |
+
"final_consensus_mean": NaN,
|
| 1701 |
+
"spectral_gap": NaN
|
| 1702 |
+
},
|
| 1703 |
+
{
|
| 1704 |
+
"study": "local_steps",
|
| 1705 |
+
"value": "6",
|
| 1706 |
+
"seeds": 5,
|
| 1707 |
+
"final_grad_norm_sq_mean": 0.0002951760642065067,
|
| 1708 |
+
"final_grad_norm_sq_std": 6.683455807540559e-08,
|
| 1709 |
+
"final_loss_mean": 0.6911896374824796,
|
| 1710 |
+
"final_loss_std": 3.369466591631232e-05,
|
| 1711 |
+
"final_consensus_mean": NaN,
|
| 1712 |
+
"spectral_gap": NaN
|
| 1713 |
+
},
|
| 1714 |
+
{
|
| 1715 |
+
"study": "participation",
|
| 1716 |
+
"value": "1",
|
| 1717 |
+
"seeds": 5,
|
| 1718 |
+
"final_grad_norm_sq_mean": 0.0002955534092699556,
|
| 1719 |
+
"final_grad_norm_sq_std": 2.5003321107893815e-07,
|
| 1720 |
+
"final_loss_mean": 0.692018887625939,
|
| 1721 |
+
"final_loss_std": 0.00019644787687949202,
|
| 1722 |
+
"final_consensus_mean": NaN,
|
| 1723 |
+
"spectral_gap": NaN
|
| 1724 |
+
},
|
| 1725 |
+
{
|
| 1726 |
+
"study": "participation",
|
| 1727 |
+
"value": "3",
|
| 1728 |
+
"seeds": 5,
|
| 1729 |
+
"final_grad_norm_sq_mean": 0.00029570157154861225,
|
| 1730 |
+
"final_grad_norm_sq_std": 1.2928937159579134e-07,
|
| 1731 |
+
"final_loss_mean": 0.6921678162240276,
|
| 1732 |
+
"final_loss_std": 7.762641835153803e-05,
|
| 1733 |
+
"final_consensus_mean": NaN,
|
| 1734 |
+
"spectral_gap": NaN
|
| 1735 |
+
},
|
| 1736 |
+
{
|
| 1737 |
+
"study": "participation",
|
| 1738 |
+
"value": "5",
|
| 1739 |
+
"seeds": 5,
|
| 1740 |
+
"final_grad_norm_sq_mean": 0.0002956447370211676,
|
| 1741 |
+
"final_grad_norm_sq_std": 7.143137704444013e-08,
|
| 1742 |
+
"final_loss_mean": 0.6921181457214483,
|
| 1743 |
+
"final_loss_std": 5.369632825832004e-05,
|
| 1744 |
+
"final_consensus_mean": NaN,
|
| 1745 |
+
"spectral_gap": NaN
|
| 1746 |
+
},
|
| 1747 |
+
{
|
| 1748 |
+
"study": "staleness",
|
| 1749 |
+
"value": "0",
|
| 1750 |
+
"seeds": 5,
|
| 1751 |
+
"final_grad_norm_sq_mean": 0.00029570157154861225,
|
| 1752 |
+
"final_grad_norm_sq_std": 1.2928937159579134e-07,
|
| 1753 |
+
"final_loss_mean": 0.6921678162240276,
|
| 1754 |
+
"final_loss_std": 7.762641835153803e-05,
|
| 1755 |
+
"final_consensus_mean": NaN,
|
| 1756 |
+
"spectral_gap": NaN
|
| 1757 |
+
},
|
| 1758 |
+
{
|
| 1759 |
+
"study": "staleness",
|
| 1760 |
+
"value": "2",
|
| 1761 |
+
"seeds": 5,
|
| 1762 |
+
"final_grad_norm_sq_mean": 0.0002958691039744208,
|
| 1763 |
+
"final_grad_norm_sq_std": 5.3671974588500576e-08,
|
| 1764 |
+
"final_loss_mean": 0.6925704904970035,
|
| 1765 |
+
"final_loss_std": 3.2284360529443266e-05,
|
| 1766 |
+
"final_consensus_mean": NaN,
|
| 1767 |
+
"spectral_gap": NaN
|
| 1768 |
+
},
|
| 1769 |
+
{
|
| 1770 |
+
"study": "staleness",
|
| 1771 |
+
"value": "5",
|
| 1772 |
+
"seeds": 5,
|
| 1773 |
+
"final_grad_norm_sq_mean": 0.00029603009742091614,
|
| 1774 |
+
"final_grad_norm_sq_std": 3.3972548051372546e-08,
|
| 1775 |
+
"final_loss_mean": 0.6928111896109521,
|
| 1776 |
+
"final_loss_std": 2.615190936965446e-05,
|
| 1777 |
+
"final_consensus_mean": NaN,
|
| 1778 |
+
"spectral_gap": NaN
|
| 1779 |
+
},
|
| 1780 |
+
{
|
| 1781 |
+
"study": "topology",
|
| 1782 |
+
"value": "path",
|
| 1783 |
+
"seeds": 5,
|
| 1784 |
+
"final_grad_norm_sq_mean": 0.0002953077578322604,
|
| 1785 |
+
"final_grad_norm_sq_std": 1.1483142849219158e-07,
|
| 1786 |
+
"final_loss_mean": 0.6914850457852572,
|
| 1787 |
+
"final_loss_std": 0.00010632600833795026,
|
| 1788 |
+
"final_consensus_mean": 0.004072685849233602,
|
| 1789 |
+
"spectral_gap": 0.23843311486114604
|
| 1790 |
+
},
|
| 1791 |
+
{
|
| 1792 |
+
"study": "topology",
|
| 1793 |
+
"value": "ring",
|
| 1794 |
+
"seeds": 5,
|
| 1795 |
+
"final_grad_norm_sq_mean": 0.0002953075298494851,
|
| 1796 |
+
"final_grad_norm_sq_std": 1.1491135702150993e-07,
|
| 1797 |
+
"final_loss_mean": 0.6914844965936869,
|
| 1798 |
+
"final_loss_std": 0.00010642678367370458,
|
| 1799 |
+
"final_consensus_mean": 0.0003035894361151224,
|
| 1800 |
+
"spectral_gap": 0.709107334583345
|
| 1801 |
+
},
|
| 1802 |
+
{
|
| 1803 |
+
"study": "topology",
|
| 1804 |
+
"value": "star",
|
| 1805 |
+
"seeds": 5,
|
| 1806 |
+
"final_grad_norm_sq_mean": 0.00029530756477476273,
|
| 1807 |
+
"final_grad_norm_sq_std": 1.1484830376721028e-07,
|
| 1808 |
+
"final_loss_mean": 0.6914853768018828,
|
| 1809 |
+
"final_loss_std": 0.00010632513365832594,
|
| 1810 |
+
"final_consensus_mean": 0.005271681629193685,
|
| 1811 |
+
"spectral_gap": 0.3599999999999999
|
| 1812 |
+
},
|
| 1813 |
+
{
|
| 1814 |
+
"study": "topology",
|
| 1815 |
+
"value": "complete",
|
| 1816 |
+
"seeds": 5,
|
| 1817 |
+
"final_grad_norm_sq_mean": 0.0002953078259770435,
|
| 1818 |
+
"final_grad_norm_sq_std": 1.1488566697882272e-07,
|
| 1819 |
+
"final_loss_mean": 0.6914845873743779,
|
| 1820 |
+
"final_loss_std": 0.00010641988295715827,
|
| 1821 |
+
"final_consensus_mean": 5.8427434688283515e-33,
|
| 1822 |
+
"spectral_gap": 1.0
|
| 1823 |
+
}
|
| 1824 |
+
]
|
| 1825 |
+
}
|
| 1826 |
+
|
| 1827 |
+
````
|
| 1828 |
+
|
| 1829 |
+
|
| 1830 |
+
---
|
| 1831 |
+
<!-- trackio-cell
|
| 1832 |
+
{"type": "artifact", "id": "cell_329a94b543a3", "created_at": "2026-07-29T13:18:18+00:00", "title": "Artifact: trajectories.csv", "path": "empirical_outputs/trajectories.csv", "size": 708279, "artifact_type": "dataset", "auto": true}
|
| 1833 |
+
-->
|
| 1834 |
+
**📦 Artifact** `empirical_outputs/trajectories.csv` · dataset · 0.7 MB
|
| 1835 |
+
|
| 1836 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/trajectories.csv
|
| 1837 |
+
|
| 1838 |
+
|
| 1839 |
+
---
|
| 1840 |
+
<!-- trackio-cell
|
| 1841 |
+
{"type": "artifact", "id": "cell_0b28e6f98950", "created_at": "2026-07-29T13:18:18+00:00", "title": "Artifact: summary.csv", "path": "empirical_outputs/summary.csv", "size": 1700, "artifact_type": "dataset", "auto": true}
|
| 1842 |
+
-->
|
| 1843 |
+
**📦 Artifact** `empirical_outputs/summary.csv` · dataset · 1.7 kB
|
| 1844 |
+
|
| 1845 |
+
https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/summary.csv
|
| 1846 |
+
|
| 1847 |
+
|
| 1848 |
+
---
|
| 1849 |
+
<!-- trackio-cell
|
| 1850 |
+
{"type": "markdown", "id": "cell_70f58acbaf16", "created_at": "2026-07-29T13:21:26+00:00", "title": "Finding"}
|
| 1851 |
+
-->
|
| 1852 |
+
**Verdict: mixed, partial support at reduced model scale.** The final run used five distinct [SHP](https://huggingface.co/datasets/stanfordnlp/SHP) domains as non-IID clients, 120 train and 40 held-out preference pairs per client, 80 rounds, and seeds 42–46. A 1024-D hashed log-linear policy replaced the paper [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2) backbone; this matches the theory policy class but is not a full LLM replication.
|
| 1853 |
+
|
| 1854 |
+
| Effect | Result |
|
| 1855 |
+
|---|---|
|
| 1856 |
+
| Local steps `E=1,3,6` | Final gradient norm² decreased `2.95984e-4 → 2.95645e-4 → 2.95176e-4`; supported in the computation-dominated regime. |
|
| 1857 |
+
| Participation `S=1,3,5` | Mean final gap was not monotone, contradicting the strong mean-floor plot claim at this scale. Across-seed std did fall `2.50e-7 → 1.29e-7 → 0.714e-7`, with positive fit versus `1/S` and `R²=0.972`; variance effect supported. |
|
| 1858 |
+
| Staleness `q=0,2,5` | Mean final gap rose monotonically; positive linear fit `R²=0.984`; supported qualitatively. |
|
| 1859 |
+
| Topology | Path `0.00407`, ring `0.000304`, star `0.00527`, complete `≈0` final consensus error; fit versus `1/(1−ρ²)` has `R²=0.680`. Partial support with a star anomaly, also noted in the paper. |
|
| 1860 |
+
|
| 1861 |
+
The [official repository at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7) does not reproduce its own documented defaults: `configs/default.yaml` and README say N=5, R=80, E=3, S=3, while runtime `distributed_dpo/config.py` has N=3, R=5, E=1, S=2 and never loads the YAML. Its staleness implementation ignores the magnitude of `q_max`. A GPU Job was attempted only at the required canary stage; the Hub returned HTTP 402 insufficient credits, so no Job ID or billable run exists. Local final wall time was 42.9 s on Apple M1; billed cost $0. Dataset revisions inspected: [SHP e94b5f3](https://huggingface.co/datasets/stanfordnlp/SHP/tree/e94b5f32602712d78ed494fe79105b1959396686) and [HH-RLHF 09be8c5](https://huggingface.co/datasets/Anthropic/hh-rlhf/tree/09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa).
|
| 1862 |
+
|
| 1863 |
+
|
| 1864 |
+
---
|
| 1865 |
+
<!-- trackio-cell
|
| 1866 |
+
{"type": "figure", "id": "cell_b69168af59f5", "created_at": "2026-07-29T13:22:53+00:00", "title": "SHP ablation summary"}
|
| 1867 |
+
-->
|
| 1868 |
+
````html
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| 1869 |
+
<html>
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| 1870 |
+
<head><meta charset="utf-8" /></head>
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| 1871 |
+
<body>
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+
<div style="height:800px; width:1200px;"> <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
|
| 1873 |
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<script charset="utf-8" src="https://cdn.plot.ly/plotly-3.7.0.min.js" integrity="sha256-jvTGqxNp8AGWEcvNLVuKr+8j5dGe9Yw51LQkmDH+IYA=" crossorigin="anonymous"></script> <div id="b352e0d4-5a60-4aab-8cc8-9aec40f8aab0" class="plotly-graph-div" style="height:100%; width:100%;"></div> <script> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("b352e0d4-5a60-4aab-8cc8-9aec40f8aab0")) { Plotly.newPlot( "b352e0d4-5a60-4aab-8cc8-9aec40f8aab0", 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|
| 1874 |
+
</body>
|
| 1875 |
+
</html>
|
| 1876 |
+
````
|
| 1877 |
+
|
| 1878 |
+
````raw
|
| 1879 |
+
{
|
| 1880 |
+
"metadata": {
|
| 1881 |
+
"scope": "scaled local proxy; real SHP, N=5, log-linear policy",
|
| 1882 |
+
"full_paper_backbone": "distilgpt2 (~82M)",
|
| 1883 |
+
"reproduction_policy": "hashed log-linear (1024 dimensions)",
|
| 1884 |
+
"dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 1885 |
+
"dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
|
| 1886 |
+
"domains": [
|
| 1887 |
+
"askacademia_train",
|
| 1888 |
+
"askbaking_train",
|
| 1889 |
+
"askcarguys_train",
|
| 1890 |
+
"askphilosophy_train",
|
| 1891 |
+
"legaladvice_train"
|
| 1892 |
+
],
|
| 1893 |
+
"n_clients": 5,
|
| 1894 |
+
"train_pairs_per_client": 120,
|
| 1895 |
+
"eval_pairs_per_client": 40,
|
| 1896 |
+
"rounds": 80,
|
| 1897 |
+
"seeds": [
|
| 1898 |
+
42,
|
| 1899 |
+
43,
|
| 1900 |
+
44,
|
| 1901 |
+
45,
|
| 1902 |
+
46
|
| 1903 |
+
],
|
| 1904 |
+
"learning_rate": 0.25,
|
| 1905 |
+
"beta": 0.2,
|
| 1906 |
+
"batch_size": 16,
|
| 1907 |
+
"wall_time_seconds": 41.70772958299494,
|
| 1908 |
+
"hardware": "Apple M1 CPU, 16 GB unified memory",
|
| 1909 |
+
"hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
|
| 1910 |
+
"billed_cost_usd": 0.0
|
| 1911 |
+
},
|
| 1912 |
+
"fits": {
|
| 1913 |
+
"participation_grad_vs_inverse_S": {
|
| 1914 |
+
"slope": -1.490254080375279e-07,
|
| 1915 |
+
"intercept": 0.0002957094078217976,
|
| 1916 |
+
"r_squared": 0.7302089505595288
|
| 1917 |
+
},
|
| 1918 |
+
"participation_seed_std_vs_inverse_S": {
|
| 1919 |
+
"slope": 2.0965986244041204e-07,
|
| 1920 |
+
"intercept": 4.309183465906812e-08,
|
| 1921 |
+
"r_squared": 0.9724121551177207
|
| 1922 |
+
},
|
| 1923 |
+
"staleness_grad_vs_q_max": {
|
| 1924 |
+
"slope": 6.475459349008947e-08,
|
| 1925 |
+
"intercept": 0.0002957158302631729,
|
| 1926 |
+
"r_squared": 0.98409493928456
|
| 1927 |
+
},
|
| 1928 |
+
"topology_consensus_vs_inverse_gap": {
|
| 1929 |
+
"slope": 0.0015142574989828385,
|
| 1930 |
+
"intercept": -0.001139720595757755,
|
| 1931 |
+
"r_squared": 0.6802636259472109
|
| 1932 |
+
}
|
| 1933 |
+
},
|
| 1934 |
+
"summary": [
|
| 1935 |
+
{
|
| 1936 |
+
"study": "local_steps",
|
| 1937 |
+
"value": "1",
|
| 1938 |
+
"seeds": 5,
|
| 1939 |
+
"final_grad_norm_sq_mean": 0.0002959844413176936,
|
| 1940 |
+
"final_grad_norm_sq_std": 3.1242630621229745e-08,
|
| 1941 |
+
"final_loss_mean": 0.692767434637361,
|
| 1942 |
+
"final_loss_std": 2.9960266660448454e-05,
|
| 1943 |
+
"final_consensus_mean": NaN,
|
| 1944 |
+
"spectral_gap": NaN
|
| 1945 |
+
},
|
| 1946 |
+
{
|
| 1947 |
+
"study": "local_steps",
|
| 1948 |
+
"value": "3",
|
| 1949 |
+
"seeds": 5,
|
| 1950 |
+
"final_grad_norm_sq_mean": 0.0002956447370211676,
|
| 1951 |
+
"final_grad_norm_sq_std": 7.143137704444013e-08,
|
| 1952 |
+
"final_loss_mean": 0.6921181457214483,
|
| 1953 |
+
"final_loss_std": 5.369632825832004e-05,
|
| 1954 |
+
"final_consensus_mean": NaN,
|
| 1955 |
+
"spectral_gap": NaN
|
| 1956 |
+
},
|
| 1957 |
+
{
|
| 1958 |
+
"study": "local_steps",
|
| 1959 |
+
"value": "6",
|
| 1960 |
+
"seeds": 5,
|
| 1961 |
+
"final_grad_norm_sq_mean": 0.0002951760642065067,
|
| 1962 |
+
"final_grad_norm_sq_std": 6.683455807540559e-08,
|
| 1963 |
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"final_loss_mean": 0.6911896374824796,
|
| 1964 |
+
"final_loss_std": 3.369466591631232e-05,
|
| 1965 |
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"final_consensus_mean": NaN,
|
| 1966 |
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"spectral_gap": NaN
|
| 1967 |
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},
|
| 1968 |
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{
|
| 1969 |
+
"study": "participation",
|
| 1970 |
+
"value": "1",
|
| 1971 |
+
"seeds": 5,
|
| 1972 |
+
"final_grad_norm_sq_mean": 0.0002955534092699556,
|
| 1973 |
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"final_grad_norm_sq_std": 2.5003321107893815e-07,
|
| 1974 |
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"final_loss_mean": 0.692018887625939,
|
| 1975 |
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"final_loss_std": 0.00019644787687949202,
|
| 1976 |
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"final_consensus_mean": NaN,
|
| 1977 |
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"spectral_gap": NaN
|
| 1978 |
+
},
|
| 1979 |
+
{
|
| 1980 |
+
"study": "participation",
|
| 1981 |
+
"value": "3",
|
| 1982 |
+
"seeds": 5,
|
| 1983 |
+
"final_grad_norm_sq_mean": 0.00029570157154861225,
|
| 1984 |
+
"final_grad_norm_sq_std": 1.2928937159579134e-07,
|
| 1985 |
+
"final_loss_mean": 0.6921678162240276,
|
| 1986 |
+
"final_loss_std": 7.762641835153803e-05,
|
| 1987 |
+
"final_consensus_mean": NaN,
|
| 1988 |
+
"spectral_gap": NaN
|
| 1989 |
+
},
|
| 1990 |
+
{
|
| 1991 |
+
"study": "participation",
|
| 1992 |
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"value": "5",
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| 1993 |
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"seeds": 5,
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| 1994 |
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"final_grad_norm_sq_std": 7.143137704444013e-08,
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| 1996 |
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"final_loss_mean": 0.6921181457214483,
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| 1997 |
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"final_loss_std": 5.369632825832004e-05,
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| 1998 |
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"final_consensus_mean": NaN,
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| 1999 |
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"spectral_gap": NaN
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| 2000 |
+
},
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| 2001 |
+
{
|
| 2002 |
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"study": "staleness",
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| 2003 |
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"value": "0",
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| 2004 |
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"seeds": 5,
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| 2005 |
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| 2006 |
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| 2008 |
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| 2009 |
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| 2010 |
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"spectral_gap": NaN
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| 2011 |
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},
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| 2012 |
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{
|
| 2013 |
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"study": "staleness",
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| 2014 |
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"value": "2",
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| 2015 |
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"seeds": 5,
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| 2016 |
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| 2019 |
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| 2020 |
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| 2022 |
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},
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| 2023 |
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{
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| 2024 |
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"study": "staleness",
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| 2025 |
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"value": "5",
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| 2026 |
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"seeds": 5,
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| 2027 |
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| 2033 |
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},
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| 2034 |
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{
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| 2035 |
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"study": "topology",
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| 2036 |
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"value": "path",
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| 2037 |
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"seeds": 5,
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| 2038 |
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| 2044 |
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},
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| 2045 |
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| 2046 |
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"study": "topology",
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| 2047 |
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"value": "ring",
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| 2048 |
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"seeds": 5,
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| 2049 |
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| 2055 |
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},
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| 2056 |
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{
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| 2057 |
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"study": "topology",
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| 2058 |
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"value": "star",
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| 2059 |
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"seeds": 5,
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| 2060 |
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| 2061 |
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| 2064 |
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|
| 2065 |
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"spectral_gap": 0.3599999999999999
|
| 2066 |
+
},
|
| 2067 |
+
{
|
| 2068 |
+
"study": "topology",
|
| 2069 |
+
"value": "complete",
|
| 2070 |
+
"seeds": 5,
|
| 2071 |
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|
| 2072 |
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| 2074 |
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| 2075 |
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|
| 2076 |
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"spectral_gap": 1.0
|
| 2077 |
+
}
|
| 2078 |
+
]
|
| 2079 |
+
}
|
| 2080 |
+
````
|
pages/conclusion/page.md
ADDED
|
@@ -0,0 +1,18 @@
|
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|
|
|
|
| 1 |
+
# Conclusion
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{"type": "markdown", "id": "cell_553cfaf65499", "created_at": "2026-07-29T13:21:27+00:00", "title": "Conclusion"}
|
| 7 |
+
-->
|
| 8 |
+
**Overall finding: the paper contains promising mechanisms but the six requested claims are not reproducible as a rigorous package from the released materials.** Claim 5 has correct statement-level rate algebra and a double-precision spectral-recursion audit. Claim 2 is only correct under the narrow interpretation that full participation removes client-sampling amplification, since stochastic variance `2Lηζ²_g/N` remains. Claims 1 and 3 have displayed statements but no active proofs and identifiable draft gaps; Claim 4 has a quantifier/monotonicity problem and no actual DPO hard-instance proof. Claim 6 receives mixed proxy support on real SHP: local steps, bounded staleness, and most topology effects agree, but the mean participation gap does not.
|
| 9 |
+
|
| 10 |
+
Reproducibility notes:
|
| 11 |
+
|
| 12 |
+
- Paper: [arXiv 2605.20696](https://arxiv.org/abs/2605.20696); [OpenReview ljNZyrAlaa](https://openreview.net/forum?id=ljNZyrAlaa).
|
| 13 |
+
- Code audit pinned to [GitHub commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).
|
| 14 |
+
- Data: [SHP](https://huggingface.co/datasets/stanfordnlp/SHP) and inspected [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf).
|
| 15 |
+
- Model named by the paper: [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2); not executed in this credit-blocked reproduction.
|
| 16 |
+
- Exact commands, scripts, outputs, CSV trajectories, raw JSON, and interactive figures are captured in this logbook. The published artifact Bucket and private trace dataset are linked automatically after publication.
|
| 17 |
+
- No Hugging Face Job exists: the mandatory canary failed before creation with HTTP 402. This is an infrastructure limitation, not a successful zero-cost Job.
|
| 18 |
+
- The local proxy intentionally improves two design issues: clients are split by domain rather than randomly, and `q_max` samples an actual bounded delay rather than acting as a Boolean.
|
pages/executive-summary/page.md
ADDED
|
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|
pages/index.md
ADDED
|
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|
|
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|
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|
|
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|
|
|
| 1 |
+
# Reproduction: Distributed Direct Preference Optimization
|
| 2 |
+
|
| 3 |
+
## Pages
|
| 4 |
+
|
| 5 |
+
| Page |
|
| 6 |
+
| --- |
|
| 7 |
+
| [Executive summary](#/executive-summary) |
|
| 8 |
+
| [Claim 1: FedDPO partial participation bound](#/claim-1-feddpo-partial-participation-bound) |
|
| 9 |
+
| [Claim 2: Full participation corollary](#/claim-2-full-participation-corollary) |
|
| 10 |
+
| [Claim 3: Staleness penalty](#/claim-3-staleness-penalty) |
|
| 11 |
+
| [Claim 4: FedDPO lower bound](#/claim-4-feddpo-lower-bound) |
|
| 12 |
+
| [Claim 5: DecDPO spectral rate](#/claim-5-decdpo-spectral-rate) |
|
| 13 |
+
| [Claim 6: SHP numerical results](#/claim-6-shp-numerical-results) |
|
| 14 |
+
| [Conclusion](#/conclusion) |
|
trackio-logo-light.png
ADDED
|
trackio-logo.png
ADDED
|
trackio-wordmark-dark.png
ADDED
|
workspace.json
ADDED
|
@@ -0,0 +1,29 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
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{
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{
|
| 8 |
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"url": "https://huggingface.co/datasets/stanfordnlp/SHP",
|
| 9 |
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"type": "Datasets",
|
| 10 |
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"label": "stanfordnlp/SHP"
|
| 11 |
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},
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| 12 |
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{
|
| 13 |
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"url": "https://huggingface.co/distilbert/distilgpt2",
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| 14 |
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"type": "Models",
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"label": "distilbert/distilgpt2"
|
| 16 |
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},
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| 17 |
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{
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| 18 |
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"url": "https://huggingface.co/datasets/Anthropic/hh-rlhf",
|
| 19 |
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"type": "Datasets",
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| 20 |
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"label": "Anthropic/hh-rlhf"
|
| 21 |
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},
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| 22 |
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{
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| 23 |
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"url": "https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/bound_terms.csv",
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| 24 |
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"type": "Buckets",
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| 25 |
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"label": "SabaPivot/repro-distributed-direct-preference-optimization-artifacts"
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| 26 |
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}
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| 27 |
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],
|
| 28 |
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"reference_only": true
|
| 29 |
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}
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