Paper finds RLVR improves sampling efficiency but not new reasoning patterns

jxmnop · x · 2026-07-28

• The post highlights a paper asking whether reinforcement learning with verifiable rewards really creates new reasoning ability in LLMs beyond the base model.
• Across multiple models and benchmarks, the authors find that RL-trained models may look better at small sample counts, but base models catch up as the number of samples grows and often surpass the RL-trained versions.
• The paper argues that current RLVR methods do not seem to unlock fundamentally new reasoning patterns; instead, they mainly improve sampling efficiency.
• One exception is distillation, which the authors say can introduce genuinely new reasoning patterns and expand capability beyond the teacher.

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