Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 16,790 Bytes
ac05fbf 84740d4 d02d724 84740d4 185cce2 84740d4 185cce2 84740d4 185cce2 84740d4 185cce2 84740d4 185cce2 84740d4 185cce2 84740d4 185cce2 84740d4 d02d724 84740d4 d02d724 84740d4 d02d724 84740d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 | """hint_generator.py — Template-based hint generator (v0.1 starter).
Composer 2.5 inserts text hints at error-turn sites:
"Reminder: Available tools are: …" (when a tool-call refs a non-existent tool)
"Reminder: tool arguments must be valid JSON" (on JSONDecodeError)
... etc.
This module provides a registry of hint templates keyed by error_kind. The
data collator (in trl_path/data_collator.py) calls dispatch(error_kind, ctx)
to get the hint text to splice into ctx_teacher.
v0.2 will replace these templates with an LLM-driven hint generator (likely
Sonnet 4.6 or Opus 4.7 via OpenRouter) for cases where templates are too rigid
(style violations, wasteful explanations).
"""
from __future__ import annotations
from collections.abc import Callable
from typing import TypedDict
class HintContext(TypedDict, total=False):
"""Per-error context the hint generator can use."""
error_kind: str # e.g. "tool_not_found", "json_decode", "type_error"
error_message: str # raw error from the env
available_tools: list[str] # for tool_not_found
tool_name: str # the failing tool, if known
tool_schema: dict # the schema, if known
intent: str # student's apparent intent, if extractable
# ---------------------------------------------------------------------------
# Hint templates
# ---------------------------------------------------------------------------
def hint_tool_not_found(ctx: HintContext) -> str:
tools = ctx.get("available_tools", [])
if tools:
tool_list = ", ".join(f"`{t}`" for t in tools)
return f"Reminder: Available tools are: {tool_list}. Please use one of these."
return "Reminder: the tool you tried to call does not exist. Use only available tools."
def hint_json_decode(ctx: HintContext) -> str:
return (
"Reminder: tool arguments must be valid JSON. Common mistakes: "
"single quotes (use double), trailing commas, unescaped newlines in strings."
)
def hint_type_error(ctx: HintContext) -> str:
name = ctx.get("tool_name")
schema = ctx.get("tool_schema")
if name and schema:
return (
f"Reminder: `{name}` expects arguments matching this schema:\n"
f" {schema}\n"
"Re-issue the call with arguments matching the schema."
)
return "Reminder: tool arguments do not match the expected types. Check the schema."
def hint_runtime_error(ctx: HintContext) -> str:
msg = ctx.get("error_message", "an exception")
return (
f"Reminder: the previous tool call raised {msg}. "
"Reconsider the inputs or read the relevant code first to understand state."
)
def hint_repeated_failure(ctx: HintContext) -> str:
"""Triggered when the same kind of error happens 3+ times in a row."""
return (
"Reminder: this approach has failed multiple times. "
"Step back and consider an alternative approach: read more files, "
"search for similar patterns elsewhere, or break the task down differently."
)
# ---------------------------------------------------------------------------
# Registry
# ---------------------------------------------------------------------------
HINT_TEMPLATES: dict[str, Callable[[HintContext], str]] = {
"tool_not_found": hint_tool_not_found,
"json_decode": hint_json_decode,
"type_error": hint_type_error,
"runtime_error": hint_runtime_error,
"repeated_failure": hint_repeated_failure,
}
def dispatch(error_kind: str, ctx: HintContext | None = None) -> str | None:
"""Generate a hint for the given error_kind. Returns None if unknown."""
fn = HINT_TEMPLATES.get(error_kind)
if fn is None:
return None
return fn(ctx or {})
def register(error_kind: str, fn: Callable[[HintContext], str]) -> None:
"""Add a custom hint template."""
HINT_TEMPLATES[error_kind] = fn
# ===========================================================================
# Layered HintGenerator architecture (ADR-009)
# ===========================================================================
#
# Composer 2.5 inserts a natural-language hint at each error turn; the
# hint-conditioned forward becomes the SDPO teacher. HOW Cursor generates the
# hint is unstated in every Cursor artifact (both blogs + the Composer 2 tech
# report, arXiv:2603.24477 — confirmed absent in research/10). So this is our
# design problem. The cited papers bracket the answer: OPSD conditions the
# teacher on ground-truth; SDPO generalizes to environment feedback and the
# "successful sibling rollout as implicit feedback" trick.
#
# We implement a layered generator, tried cheapest-first:
# 1. TemplateHintGenerator — the registry above (free, deterministic;
# covers tool-error classes). The first layer.
# 2. RawErrorHintGenerator — wrap the raw env/tool error text as the hint
# (free; covers any error with a message but unmatched by a template).
# 3. LLMJudgeHintGenerator — an LLM produces a <=2-sentence corrective hint
# (cost ~$0.0005/site; covers style/communication/effort sites templates
# can't). Cached on disk; optional; OFF unless a client is provided.
# 4. (sibling-bootstrap) — RL-rollout-path only; not a HintContext-driven
# layer (needs sibling rollouts), exposed as a flag for the trainer to use.
#
# All layers satisfy the HintGenerator Protocol and compose via
# CompositeHintGenerator, whose .as_collator_hook() returns a callable matching
# the collator's existing `hint_generator: Callable[[str, dict], str | None]`
# hook — ZERO collator change.
from typing import Protocol, runtime_checkable
@runtime_checkable
class HintGenerator(Protocol):
"""A hint source. Returns hint text for an error context, or None to defer
to the next layer."""
def generate(self, error_kind: str, error_meta: dict) -> str | None: ...
class TemplateHintGenerator:
"""Layer 1: the existing template registry. Free, deterministic.
Preserves the exact behavior of the module-level `dispatch()` so existing
callers and tests see no change.
"""
def generate(self, error_kind: str, error_meta: dict) -> str | None:
# `dispatch` reads HintContext keys; error_meta IS that context dict
# plus the kind. Merge so templates that read `error_kind` still work.
ctx: HintContext = dict(error_meta) # type: ignore[assignment]
ctx.setdefault("error_kind", error_kind)
return dispatch(error_kind, ctx)
class RawErrorHintGenerator:
"""Layer 2: use the raw env/tool error text itself as the hint.
Covers any error site that carries a message but isn't matched by a
template. Free. SDPO's "environment feedback as the conditioning signal"
(arXiv:2601.20802) — the rawest form of that.
"""
def __init__(self, max_chars: int = 500) -> None:
self.max_chars = max_chars
def generate(self, error_kind: str, error_meta: dict) -> str | None:
msg = error_meta.get("error_message") or error_meta.get("error") or ""
msg = str(msg).strip()
if not msg:
return None
truncated = msg[: self.max_chars]
return f"Reminder: the previous action produced this error:\n{truncated}\nReconsider and retry."
# ---------------------------------------------------------------------------
# Error-kind routing (ADR-012 finding #2)
# ---------------------------------------------------------------------------
#
# The default composite is template -> raw-error -> judge. The raw-error layer
# fires for ANY kind carrying a message — including style/communication/effort
# sites, which are EXACTLY what the LLM judge exists to cover. So we route:
# tool/runtime error kinds may use the raw-error layer; style/communication/
# effort kinds skip it and fall through to the judge.
# Error kinds that genuinely describe a tool/runtime failure whose raw text is a
# useful, self-contained hint. The explicit registry-template kinds are included
# so behavior is unchanged for them.
_TOOL_RUNTIME_KINDS: frozenset[str] = frozenset({
"tool_not_found",
"json_decode",
"type_error",
"runtime_error",
"repeated_failure",
})
# Substrings marking a kind as tool/runtime-ish even if not explicitly listed
# (keeps generic "*_error"/"*_exception" sites flowing through raw-error, which
# is where their raw text belongs).
_TOOL_RUNTIME_MARKERS: tuple[str, ...] = (
"error", "exception", "fail", "decode", "timeout", "traceback",
"exit_code", "nonzero", "syntax", "import", "assertion", "tool",
"runtime", "crash", "exec",
)
# Substrings marking a kind as a style/communication/effort site — the judge's
# domain. These take precedence: a kind matching one of these skips raw-error.
_STYLE_KINDS_MARKERS: tuple[str, ...] = (
"style", "communic", "verbose", "effort", "concise", "tone",
"format", "wordy", "rambl", "explanation", "etiquette", "clarity",
)
def is_tool_runtime_kind(error_kind: str) -> bool:
"""True if `error_kind` is a tool/runtime failure that the raw-error layer
may serve. Style/communication/effort kinds return False (-> judge)."""
k = (error_kind or "").lower()
if any(m in k for m in _STYLE_KINDS_MARKERS):
return False
if k in _TOOL_RUNTIME_KINDS:
return True
return any(m in k for m in _TOOL_RUNTIME_MARKERS)
class RoutingHintGenerator:
"""Wraps an inner layer (the raw-error layer) and only lets it fire for
tool/runtime error kinds. For style/communication/effort kinds it returns
None so the composite falls through to the judge — the layer those sites
were always meant to reach (ADR-012 finding #2).
"""
def __init__(self, inner: HintGenerator, route=is_tool_runtime_kind) -> None:
self.inner = inner
self.route = route
def generate(self, error_kind: str, error_meta: dict) -> str | None:
if not self.route(error_kind):
return None
return self.inner.generate(error_kind, error_meta)
class LLMJudgeHintGenerator:
"""Layer 3: an LLM produces a short corrective hint.
Covers style/communication/effort sites that templates can't. Optional and
OFF unless a `complete` callable is provided. Results are cached on disk
keyed on a hash of the error context (so repeated identical sites cost
nothing after the first).
`complete(prompt: str) -> str` is an injected text-completion callable
(e.g. an OpenRouter chat wrapper). Kept abstract so this module has no hard
network dependency and is unit-testable with a stub.
"""
PROMPT_TEMPLATE = (
"An autonomous coding agent made a mistake at one step of a trajectory. "
"Write a SHORT (<=2 sentences) corrective hint that, if the agent had "
"seen it, would steer it to the right behavior for THIS step only. Do "
"not solve the whole task; just correct the local mistake.\n\n"
"Error kind: {error_kind}\n"
"Error / context:\n{error_message}\n\n"
"Corrective hint:"
)
# Bump when PROMPT_TEMPLATE or the underlying judge model changes so stale
# cached hints are invalidated rather than silently reused.
_CACHE_VERSION = 2
# Hard cap on a generated hint. The judge is asked for <=2 sentences but
# nothing enforced it (cross-family review 2026-05-29) — a runaway judge
# could emit a full solution / prompt-leak / megabyte of text straight into
# the SDPO teacher conditioning. Clamp defensively.
_MAX_HINT_CHARS = 600
def __init__(
self,
complete: Callable[[str], str] | None = None,
*,
cache_dir: str | None = None,
) -> None:
self.complete = complete
self._cache_dir = cache_dir
self._mem_cache: dict[str, str] = {}
def _cache_key(self, error_kind: str, error_meta: dict) -> str:
import hashlib
import json
import re
# Strip volatile object reprs (e.g. "<Exception at 0x7f8b...>") so the
# key is stable across runs/restarts. Cross-family review 2026-05-29:
# `default=str` on raw Exception/context objects embedded a memory
# address in the key, guaranteeing a 0% cross-process cache-hit rate and
# unbounded judge cost. Also version the key so prompt/model changes
# invalidate stale hints rather than serving them.
blob = json.dumps(
{"v": self._CACHE_VERSION, "k": error_kind, "m": error_meta},
sort_keys=True, default=str,
)
blob = re.sub(r"0x[0-9a-fA-F]+", "0xADDR", blob)
blob = re.sub(r"\bat 0xADDR\b", "", blob)
return hashlib.sha256(blob.encode("utf-8")).hexdigest()[:32]
def _disk_get(self, key: str) -> str | None:
if not self._cache_dir:
return None
from pathlib import Path
p = Path(self._cache_dir) / f"{key}.txt"
return p.read_text(encoding="utf-8") if p.exists() else None
def _disk_put(self, key: str, value: str) -> None:
if not self._cache_dir:
return
import os
from pathlib import Path
d = Path(self._cache_dir)
d.mkdir(parents=True, exist_ok=True)
# Atomic write: concurrent DDP workers writing the same key would
# otherwise interleave and corrupt the file (cross-family review).
tmp = d / f"{key}.txt.{os.getpid()}.tmp"
tmp.write_text(value, encoding="utf-8")
os.replace(tmp, d / f"{key}.txt")
def generate(self, error_kind: str, error_meta: dict) -> str | None:
if self.complete is None:
return None # judge disabled — defer
key = self._cache_key(error_kind, error_meta)
if key in self._mem_cache:
return self._mem_cache[key]
cached = self._disk_get(key)
if cached is not None:
self._mem_cache[key] = cached
return cached
prompt = self.PROMPT_TEMPLATE.format(
error_kind=error_kind,
error_message=str(error_meta.get("error_message")
or error_meta.get("error") or "(no message)")[:1000],
)
hint = self.complete(prompt).strip()
if not hint:
return None
# Clamp to a sane length so a runaway judge can't inject a full solution
# or megabyte blob into the SDPO teacher conditioning (cross-family review).
if len(hint) > self._MAX_HINT_CHARS:
hint = hint[: self._MAX_HINT_CHARS].rstrip() + "…"
self._mem_cache[key] = hint
self._disk_put(key, hint)
return hint
class CompositeHintGenerator:
"""Tries each layer in order, returning the first non-None hint.
Order is cost-ascending: templates (free) -> raw error (free) -> LLM judge
(paid, optional). The first layer to produce a hint wins, so the common
tool-error case never reaches the LLM.
"""
def __init__(self, layers: list[HintGenerator]) -> None:
self.layers = layers
def generate(self, error_kind: str, error_meta: dict) -> str | None:
for layer in self.layers:
hint = layer.generate(error_kind, error_meta)
if hint is not None:
return hint
return None
def as_collator_hook(self) -> Callable[[str, dict], str | None]:
"""Return a callable matching CollatorConfig.hint_generator's signature
(error_kind, error_meta) -> str | None. ZERO collator change."""
return self.generate
def default_composite(
*,
llm_complete: Callable[[str], str] | None = None,
cache_dir: str | None = None,
enable_raw_error: bool = True,
) -> CompositeHintGenerator:
"""Build the recommended layered generator: templates -> raw-error -> judge.
The raw-error layer is wrapped in a RoutingHintGenerator so it only fires for
tool/runtime error kinds; style/communication/effort kinds skip it and fall
through to the LLM judge (ADR-012 finding #2). The LLM-judge layer is
included only when `llm_complete` is provided.
"""
layers: list[HintGenerator] = [TemplateHintGenerator()]
if enable_raw_error:
layers.append(RoutingHintGenerator(RawErrorHintGenerator()))
if llm_complete is not None:
layers.append(LLMJudgeHintGenerator(llm_complete, cache_dir=cache_dir))
return CompositeHintGenerator(layers)
__all__ = [
"dispatch",
"register",
"HintContext",
"HINT_TEMPLATES",
# Layered architecture (ADR-009)
"HintGenerator",
"TemplateHintGenerator",
"RawErrorHintGenerator",
"RoutingHintGenerator",
"is_tool_runtime_kind",
"LLMJudgeHintGenerator",
"CompositeHintGenerator",
"default_composite",
]
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