4 papers
TTPO: Test-Time Policy Optimization
Aozhe Wang, Zhengxi Lu, Jianze Wang +8
Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathematical reasoning for large l…
SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning
Jianze Wang, Kunwang Zheng, Ying Liu +5
Test-time reinforcement learning (TTRL) enables language models to self-evolve at inference time without labeled feedback. Existing methods rely on answer voting and therefore do n…
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:…
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…