chain-of-thought ↔ reinforcement learning

Relations (1)

related 2.00 — strongly supporting 3 facts

Reinforcement learning is used to optimize and train chain-of-thought reasoning processes, as seen in the reinforcement of reasoning traces [1], the impact of RL mechanisms on chain-of-thought length [2], and the integration of chain-of-thought with reward-based frameworks like RL4HS [3].

Facts (3)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 1 fact

claimFan et al. (2025) attribute the tendency of reasoning models to fall into redundant loops of self-doubt and hallucination to current Reinforcement Learning (RL) mechanisms that over-reward detailed Chain-of-Thought.

EdinburghNLP/awesome-hallucination-detection - GitHub github.com GitHub 1 fact

referenceRL4HS is a reinforcement-learning framework for span-level hallucination detection that couples chain-of-thought reasoning with span-level rewards, utilizing Group Relative Policy Optimization (GRPO) and Class-Aware Policy Optimization (CAPO) to address reward imbalance between hallucinated and non-hallucinated spans.

LLM Hallucination Detection and Mitigation: State of the Art in 2026 zylos.ai Zylos 1 fact

claimOpenAI's 2026 research on reasoning models demonstrates that naturally understandable chain-of-thought reasoning traces are reinforced through reinforcement learning, and that simple prompted GPT-4o models can effectively monitor for reward hacking in frontier reasoning models like o1 and o3-mini successors.