Direct Nash Optimization aligns language models with general preferences
DNO-Prct iterates on-policy sampling, rating, and contrastive updates to raise length-controlled win rates on AlpacaEval 2.0 for Llama 3.1 8B and Mistral 7B Instruct models.
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DNO-Prct iterates on-policy sampling, rating, and contrastive updates to raise length-controlled win rates on AlpacaEval 2.0 for Llama 3.1 8B and Mistral 7B Instruct models.
The template-based pipeline produces a 60-language corpus for safety training, exposing gaps in English-only evaluation.
LexReward defines taxonomy-driven rewards over Style, Element, and Chain dimensions for legal LLMs, feeding DPO and per-dimension reward models for RL.
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