Recent breakthroughs in reasoning-focused large language models (LLMs) like OpenAI-o1, DeepSeek-R1, and Kimi-1.5 have largely relied on Reinforcement Learning with Verifiable Rewards (RLVR), which replaces human annotations with automated rewards (e.g., verified math solutions or passing code tests) to scale self-improvement. While RLVR enhances reasoning behaviors such as self-reflection and iterative refinement, we challenge a core assumption:
Does RLVR actually expand LLMs’ reasoning capabilities, or does it merely optimize existing ones?
By evaluating models via pass@k, where success requires just one correct solution among k attempts, we uncover that RL-trained models excel at low k (e.g., pass@1) but are consistently outperformed by base models at high k (e.g., pass@256). This demonstrates that RLVR narrows the model’s exploration, favoring known high-reward paths instead of discovering new reasoning strategies. Crucially, all correct solutions from RL-trained models already exist in the base model’s distribution, proving RLVR enhances sampling efficiency, not reasoning capacity, while inadvertently shrinking the solution space.
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
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