Paper proposes RL budget control to cap reasoning effort and save tokens
stochasticchasm · x · 2026-07-28
The screenshot shows a paper section titled Reasoning Effort RL.
It describes a per-problem budget-control mechanism used during RL fine-tuning to maximize token efficiency:
- Each problem is assigned an initial token budget estimated from a cold-start model.
- The model is rewarded when trajectories stay within the budget.
- The budget is adjusted with a multiplier \(\tau\), with a curriculum that starts with a higher max budget and then anneals downward.
- The method is tuned per domain with human-in-the-loop guidance.
- Trajectories across reasoning levels are collected for supervised fine-tuning and multi-teacher on-policy distillation.
The key idea is to control reasoning effort explicitly rather than letting the model overthink freely.
Related event: Kimi K3 Reproduces RLVR Research and Introduces Reasoning Budget Control(4 posts)→
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