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arXiv · 2610.25860

Adaptive Decoding Methods for Token-Efficient Reinforcement Learning

Zhaonan Li·Zelei Cheng·Amritansh Mishra·Chia-Hsuan Lee·Mann Patel·Sambit Sahu·Ben Zhou·William M. Campbell
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Abstract

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 baseline, the strongest fixed-support setting in our sweep. Follow-up experiments suggest that late widening to full support is the most promising variant, although the evidence remains noisy, and mixed-reward-group diagnostics do not clearly explain the gain. An exploratory single-run test replay yields identical point estimates for the schedule and fixed $K=10$. Overall, our results show that rollout support is an important control for sampling efficiency in RLVR and motivate further study of adaptive support schedules.

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Zhaonan Li, Zelei Cheng, Amritansh Mishra, Chia-Hsuan Lee, Mann Patel, Sambit Sahu, Ben Zhou, William M. Campbell. 2026-10-19. Adaptive Decoding Methods for Token-Efficient Reinforcement Learning. https://arxiv.org/abs/2610.25860

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