aiqbench analysis
BASIS introduces a critic-free post-training algorithm that resolves the efficiency tradeoff in reinforcement learning with verifiable rewards by leveraging batch information from single-rollout samples. This approach significantly reduces mean squared error in value function estimation while achieving policy optimization performance comparable to multi-rollout baselines using substantially less training time. The method represents a notable advancement in scaling efficient reasoning capabilities for large language models without relying on traditional critic networks.
BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoning
arXiv:2605.27293v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existing algorithms face a tradeoff between computational efficiency and sample efficiency in value estimation and policy learning. We introduce BASIS, a critic-free post-training algorithm designed to address this tradeoff. At each online training step, BASIS samples only one rollout per prompt, but leverages rich information across prompts in the entire batch to improve value function estimation. Our experiments demonstrate that BASIS reduces MSE in value function estimation by 69% compared to REINFORCE++, a representative single-rollout baseline, and achieves lower MSE with one rollout than group mean estimators with 8 rollouts. This improvement in value estimation translates to better policy optimization: using substantially less training time, BASIS achieves performance close to multi-rollout GRPO-type baselines and often outperforms single-rollout REINFORCE-type baselines.
Abstract:Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existing algorithms face a tradeoff between computational efficiency and sample efficiency in value estimation and policy learning. We introduce BASIS, a critic-free post-training algorithm designed to address this tradeoff. At each online training step, BASIS samples only one rollout per prompt, but leverages rich information across prompts in the entire batch to improve value function estimation. Our experiments demonstrate that BASIS reduces MSE in value function estimation by 69% compared to REINFORCE++, a representative single-rollout baseline, and achieves lower MSE with one rollout than group mean estimators with 8 rollouts. This improvement in value estimation translates to better policy optimization: using substantially less training time, BASIS achieves performance close to multi-rollout GRPO-type baselines and often outperforms single-rollout REINFORCE-type baselines.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2605.27293 [cs.LG]
(or arXiv:2605.27293v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27293 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 233 words
