logbook.json

{
  "schema_version": 1,
  "title": "Repro - Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling",
  "emoji": "\ud83d\udd2c",
  "space_id": "bertfil/DfhMMHXDuu",
  "paper": {
    "arxiv_id": "2602.10623",
    "openreview_id": "DfhMMHXDuu",
    "title": "Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling",
    "url": "https://arxiv.org/abs/2602.10623"
  },
  "tags": [
    "icml2026-repro",
    "paper-DfhMMHXDuu"
  ],
  "updated_at": "2026-07-20T03:52:36+00:00",
  "root": {
    "slug": "index",
    "title": "Repro - Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "00-scored-evidence-summary",
        "title": "00 - Scored evidence summary",
        "file": "pages/00-scored-evidence-summary/page.md",
        "children": []
      },
      {
        "slug": "claim-1-bnrm-replaces-deterministic-scalar-reward-outputs",
        "title": "Claim 1 - BNRM replaces deterministic scalar reward outputs with spars\u2026",
        "file": "pages/claim-1-bnrm-replaces-deterministic-scalar-reward-outputs/page.md",
        "children": []
      },
      {
        "slug": "claim-2-bnrm-extends-the-bradley-terry-preference-model-wi",
        "title": "Claim 2 - BNRM extends the Bradley-Terry preference model with Gamma p\u2026",
        "file": "pages/claim-2-bnrm-extends-the-bradley-terry-preference-model-wi/page.md",
        "children": []
      },
      {
        "slug": "claim-3-with-40k-training-examples-bt-bnrm-improves-over-t",
        "title": "Claim 3 - With 40K training examples, BT-BNRM improves over the baseli\u2026",
        "file": "pages/claim-3-with-40k-training-examples-bt-bnrm-improves-over-t/page.md",
        "children": []
      },
      {
        "slug": "claim-4-bnrm-matches-the-performance-of-a-baseline-trained",
        "title": "Claim 4 - BNRM matches the performance of a baseline trained on 20K sa\u2026",
        "file": "pages/claim-4-bnrm-matches-the-performance-of-a-baseline-trained/page.md",
        "children": []
      },
      {
        "slug": "claim-5-bnrm-reduces-the-pearson-correlation-between-rewar",
        "title": "Claim 5 - BNRM reduces the Pearson correlation between reward and resp\u2026",
        "file": "pages/claim-5-bnrm-reduces-the-pearson-correlation-between-rewar/page.md",
        "children": []
      },
      {
        "slug": "claim-6-in-rlhf-policy-evaluation-policies-trained-with-bn",
        "title": "Claim 6 - In RLHF policy evaluation, policies trained with BNRM-derive\u2026",
        "file": "pages/claim-6-in-rlhf-policy-evaluation-policies-trained-with-bn/page.md",
        "children": []
      },
      {
        "slug": "reproduction-protocol-and-provenance",
        "title": "Reproduction protocol and provenance",
        "file": "pages/reproduction-protocol-and-provenance/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 5888,
  "revision": "1784519556257555900"
}