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cs.CL2026
Context-Aware RL for Agentic and Multimodal LLMs
Peiyang Xu, Bangzheng Li, Sijia Liu +4
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool…
cs.CL2025
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…
cs.CL2025
CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling
Chongyu Fan, Yihua Zhang, Jinghan Jia +2
Large reasoning models (LRMs), such as OpenAI's o1 and DeepSeek-R1, harness test-time scaling to perform multi-step reasoning for complex problem-solving. This reasoning process, e…