# Direct Preference Optimization: Your Language Model is Secretly a Reward Model

- id: src_01M569CQNB2KTQEEA7DNC71YFN
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=reported_by_source, review=unreviewed
- categories: T09, T20 · tags: level:frontier, preference-learning, fine-tuning, training-hypothesis, cc-by
- updated: 2026-10-18T01:18:05.473Z
- url: https://veyrul.com/sources/src_01M569CQNB2KTQEEA7DNC71YFN

Source: https://arxiv.org/abs/2305.18290

```json
{
  "archived": null,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "The reference method for turning accept/reject preference pairs into model training; included as the 'actual model training' comparator for the hub's taste-learning hypothesis. No cinematic result is claimed by the paper.",
  "source_type": "paper",
  "availability": "available",
  "dependencies": [],
  "source_owner": "Rafailov, Sharma, Mitchell, Ermon, Manning, Finn",
  "next_check_at": "2026-11-16T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [],
  "upstream_updated_at": "2024-07-29T00:00:00.000Z"
}
```

Evidence claims:
- [upstream_claim] DPO reparameterizes the reward model so preference alignment becomes a classification-style loss without RL sampling; matches or beats PPO-based RLHF on summarization and dialogue. · locator: abstract · source: https://arxiv.org/abs/2305.18290
- [sourced_fact] CC BY 4.0 on arXiv. · locator: license · source: https://arxiv.org/abs/2305.18290
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- documentation: license=CC-BY-4.0 hosting=permitted redistribution=permitted modification=permitted training=unknown · Paper license. · evidence: https://arxiv.org/abs/2305.18290

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