5 papers · 1 filter
Harmful Content Is Not Enough: Continuation Framing Moderates In-Context Emergent Misalignment
Peiyang Liu, Xi Wang, Ziqiang Cui +2
In-context learning (ICL) can induce emergent misalignment (EM), where narrow misaligned examples alter answers to unrelated questions. Existing prompts, however, conflate harmful-…
Progress- and Reliability-Oriented Group Policy Optimization for Agentic Reinforcement Learning
Mingxuan Fan, Peiyang Liu
Group-based reinforcement learning (RL) has become an effective paradigm for improving large language model agents on long-horizon interactive tasks. To obtain finer-grained policy…
Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories
Peiyang Liu, Zhirui Chen, Xi Wang +4
Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain inefficient. Standard approaches ret…
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation
Long Li, Zhijian Zhou, Jiangxuan Long +5
Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck is the credit assignme…
Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling
Xiaoyu Liu, Di Liang, Chang Dai +9
Reward Models (RMs) are key components for evaluating and guiding language model outputs. However, traditional scalar RMs often struggle with incorporating contextual and backgroun…