Author

Congming Qin

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Preprint Aug 2026

How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning

FACTOR is introduced, which uses checkpoint-calibrated TD residuals to assign per-action credits that telescope to the trajectory advantage, and feedback-conditioned teacher-student likelihood gaps to allocate each credit across the realized action tokens.

Li-Chao Ma, Yang Sun, Shuai Zhao et al. · 3 citations

#artificial intelligence May 2026

Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning

SelSkill is proposed, a dual-granularity preference-learning framework for selective skill invocation that formulates skill use as a skill-or-skip decision, uses predictive uncertainty to prioritize candidate decision points, and constructs controlled invoke-skip preference pairs from shared trajectory prefixes.

Chishui Chen, Jiaye Lin, Te Sun et al. · arXiv.org · 1 citation · ⚡1

Preprint Aug 2026

Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation

Observation-Calibrated Self-Distillation (OCSD), which contrasts two structurally matched replay views, Full and Observation-Ablated, to derive an observation residual that discounts score changes shared by the replay scaffold, and applies this residual to modulate token-level GRPO updates at high-uncertainty steps, wh...

Yi Yang, Congming Qin, Xiaodan Liu et al. · 4 citations