Adaptive Decoding Methods for Token-Efficient Reinforcement Learning
Rollout decoding affects both which trajectories are used for reinforcement learning with verifiable rewards (RLVR) and how many completion tokens are spent collecting them. We study whether candidate-token support should remain fixed or widen during training in the context of rollout sampling efficiency. Our main schedule moves from top-K=10 to K=20 and then to full support as the attempted-token budget increases. In a 4B code-RLVR setting, the schedule has a higher interpolated 150M-token validation value than its matched fixed-K=10 continuation in all three runs, with a mean gain of $1.80{\pm}0.42$ points. The schedule also performs comparably to a separately trained fixed-K=20 baselin...
| Authors | Zhaonan Li, Zelei Cheng, Amritansh Mishra et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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