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Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models
Dayu Wang, Jiaye Yang, Weikang Li +4
Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in interme…
It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches
Dayu Wang, Jiaye Yang, Weikang Li +4
Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrast…
Stay in Character, Stay Safe: Dual-Cycle Adversarial Self-Evolution for Safety Role-Playing Agents
Mingyang Liao, Yichen Wan, shuchen wu +6
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky o…
Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration
Dayu Wang, Jiaye Yang, Weikang Li +4
Large Language Models (LLMs) often suffer from ''Reasoning Collapse'' on challenging mathematical reasoning tasks, where stochastic sampling produces lexical variations of the same…
Probabilistic Modeling of Intentions in Socially Intelligent LLM Agents
Feifan Xia, Yuyang Fang, Defang Li +5
We present a probabilistic intent modeling framework for large language model (LLM) agents in multi-turn social dialogue. The framework maintains a belief distribution over a partn…
Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
Dayu Wang, Jiaye Yang, Weikang Li +2
Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, howev…