1 citations · 2 across the 22 of their papers we have counts for
5 papers · 1 filter
Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning
Jiangnan Xia, Yucheng Shi, Yu Yang +3
Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discovering effective solution traject…
Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
Yucheng Shi, Zhenwen Liang, Kishan Panaganti +3
We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it. In z…
Guided Self-Evolving LLMs with Minimal Human Supervision
Wenhao Yu, Zhenwen Liang, Chengsong Huang +4
AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experien…
Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning
Rui Liu, Dian Yu, Tong Zheng +8
Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inpu…
MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation
Zhenwen Liang, Linfeng Song, Yang Li +4
Automated Theorem Proving (ATP) in formal languages remains a formidable challenge in AI, demanding rigorous logical deduction and navigating vast search spaces. While large langua…