Author

D. Liang

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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

Preprint Aug 2026

PlanPO: Group Planning-Aware Policy Optimization for Multi-Turn Agentic LLMs

Group Planning-aware Policy Optimization (PlanPO) is proposed, a simple yet effective RL method for learning generalizable planning abilities beyond task-specific high-quality behavior patterns that enables agents to actively learn generalizable and deliberate behaviors spanning interaction planning and textual generation from high-quality rollouts, without degenerating into vanilla length minimization.

D. Liang, Liyuan He, Xuan Feng et al. · 0 citations