4 papers
Beyond SFT: Reinforcement Learning for Safer Large Reasoning Models with Better Reasoning Ability
Jinghan Jia, Nathalie Baracaldo, Sijia Liu
Large reasoning models (LRMs) extend large language models by generating explicit chain-of-thought (CoT) reasoning, significantly improving mathematical and logical problem solving…
EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation
Jinghan Jia, Hadi Reisizadeh, Chongyu Fan +3
Large language models (LLMs) have shown remarkable reasoning capabilities when trained with chain-of-thought (CoT) supervision. However, the long and verbose CoT traces, especially…
Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills
Changsheng Wang, Chongyu Fan, Yihua Zhang +5
Recent advances in large reasoning models (LRMs) have enabled strong chain-of-thought (CoT) generation through test-time computation. While these multi-step reasoning capabilities…
BLUR: A Bi-Level Optimization Approach for LLM Unlearning
Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu +6
Enabling large language models (LLMs) to unlearn knowledge and capabilities acquired during training has proven vital for ensuring compliance with data regulations and promoting et…