Author

Lang Feng

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Preprint Aug 2026

SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning

This work proposes Single-rollout Autoregressive Policy Optimization (SAPO), a low-memory and compute-efficient framework in which the policy and value functions share a single autoregressive backbone, and introduces a trajectory-level generalized advantage estimator that combines lambda-returns with batch normalization.

D. Liang, Lang Feng, Bo An et al. · 1 citation