3 papers
cs.CL2026
SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning
Jianze Wang, Kunwang Zheng, Ying Liu +5
The paper introduces SERPO, a test-time reinforcement learning approach that lets language models self‑improve during inference by jointly evolving response evidence, query‑specifi…
cs.CL2026
MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
Jianze Wang, Ying Liu, Jinlong Chen +7
On-policy distillation (OPD) trains a student on its own trajectories under token-level teacher supervision, but existing methods are capped by a single-teacher capability ceiling:…
cs.CR2026
SRTJ: Self-Evolving Rule-Driven Training-Free LLM Jailbreaking
Jindong Li, Ying Liu, Yali Fu +4
LLMs are increasingly equipped with safety alignment mechanisms, yet recent studies demonstrate that they remain vulnerable to jailbreaking attacks that elicit harmful behaviors wi…