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cs.CL2026
RLVR Training of LLMs Does Not Improve Thinking Ability for General QA: Evaluation Method and a Simple Solution
Kaiyuan Li, Jing-Cheng Pang, Yang Yu
Reinforcement learning from verifiable rewards (RLVR) stimulates the thinking processes of large language models (LLMs), substantially enhancing their reasoning abilities on verifi…
cs.CL2025
Controlling Large Language Model with Latent Actions
Chengxing Jia, Ziniu Li, Pengyuan Wang +4
Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the struc…
cs.CL2024
BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation
Chengxing Jia, Pengyuan Wang, Ziniu Li +4
Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applicat…