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.
More from Research
- MIT's Neil Thompson: AI comes for tasks before jobs, and fixed costs favor big firms — bigdata · 2026-10-16
- UMD's PAIR framework bridges perception and action in VLA models via bridge tokens — Kaixi Feng · 2026-10-16
- CI-MSE offline metric hits -0.87 Spearman correlation with real robot task success vs weak raw MSE — chris_j_paxton · 2026-10-16
- Colossal Biosciences uses AI to decode genetic switches behind the extinct dodo — castrotech · 2026-10-16
- Colossal Biosciences uses AI to identify gene switches in dodo de-extinction push — castrotech · 2026-10-16
- Cheng Lou open-sources Handwired: hand-written weights turn code into training-free neural nets — dotey · 2026-10-16