Author

Mao-Qi Liu

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#artificial intelligence Preprint Sep 2026

T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, rewards in many interactive tasks reflect only the final outcome, providing limited guidance on which intermediate decisions advance the task. Successful training trajecto...

Bo-Wen Zhang, Jun-Wei He, Mao-Qi Liu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics

Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the verdicts are aggregated into a reward, most often by a weighted sum. We show that this additive aggregation is the weak point. Under a sum, criteria compensate for one anot...

Mao-Qi Liu, Jun-Wei He, Bo-Wen Zhang et al. · 0 citations