AIBullisharXiv – CS AI · Apr 137/10
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The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs
Researchers introduce the Two-Stage Decision-Sampling Hypothesis to explain how reinforcement learning enables self-reflection capabilities in large language models, demonstrating that RL's superior performance stems from improved decision-making rather than generation quality. The theory shows that reward gradients distribute asymmetrically across policy components, explaining why RL succeeds where supervised fine-tuning fails.