🎮 Reinforcement LearningID:
cot-reasoningCoT & Reasoning RL
思维链与推理强化
🎯Core Definition
CoT (Chain-of-Thought) decomposes hard problems into explicit intermediate reasoning steps at inference time; RL-fortified reasoning (RLVR+GRPO) instead bakes long-chain reasoning into the parameters at training time — CoT is the decoding-time behavior those models exhibit, and the two complement each other.
💡Use Cases
multi-step reasoning (math, logic, code); in RL, CoT is the policy's generation form and RL optimizes its distribution and test-time scaling.
⚡Key Problems Solved
single-step big-O reasoning is error-prone and unlocalizable; CoT decomposition plus outcome/process rewards mitigate error propagation and credit assignment. See the LLM module: full content in the LLM module's cot card (guide reasoning-and-cot).
🎯5 High-Frequency Exam Points
1
Relation between CoT and RL reasoning training (RLVR+GRPO): test-time vs training-time methods?
2
Why do RL reasoning models produce explicit reasoning chains? CoT's role in the policy?
3
CoT emergence vs model size? Why do small models gain little?
4
How does self-consistency combine with CoT/RL to boost accuracy?
5
CoT limitations: when is it ineffective or harmful? How does test-time compute help?
📖 In-depth Guide:📄 reasoning-and-cot →
Updated 2026-08-12🎯
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