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cs.AI2026

When Refusal Looks Safe: The Refusal-Cue Shortcut in Safety Guard Models

Yu Feng, Chunting Zang, Chen Shen +4

Safety guards are widely used to filter harmful content and are typically trained via supervised fine-tuning on labeled prompt-response pairs. We audit two widely used safety-guard…

cs.AI2026

Purified OPSD: On-Policy Self-Distillation Without Losing How to Think

Zhanming Shen, Jintao Tong, Shaotian Yan +9

On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-lev…

cs.AI2026

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing

Yuanpeng Li, Gefei Lin, Annie Qu +1

Soft Actor-Critic (SAC) and its variants dominate Multi-Task Reinforcement Learning (MTRL) due to their off-policy sample efficiency, while on-policy methods such as Proximal Polic…

cs.AI2026

ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning

Yu Li, Rui Miao, Zhengling Qi +1

The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods treat each problem inst…

cs.AI2025

Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style

Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4

Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…