Reasoning language models extract answers from sparse, order-shuffled chain-of-thought traces with little accuracy loss.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=
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SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
RLAIF matches RLHF on summarization and dialogue tasks, with a direct-RLAIF variant achieving superior results by using LLM rewards directly during training.
Defines Entropy-Gradient Inversion as a negative entropy-gradient correlation fingerprinting LRM reasoning and proposes CorR-PO to embed it in RL regularization, claiming consistent outperformance on benchmarks.
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RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
RLAIF matches RLHF on summarization and dialogue tasks, with a direct-RLAIF variant achieving superior results by using LLM rewards directly during training.