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Chengyu Shen

2 accepted papers

2026

Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions

ICLR 2026poster

Recent advances in large language model (LLM) reasoning have shown that reasoning ability can emerge through reinforcement learning (RL). However, despite these successes, RL in its current form remains insufficient to induce capabilities that exceed the limitations of the base model, as it is prima…

Cited by 0SourcecodeScholar
2024

Autonomous Interactive Correction MLLM for Robust Robotic Manipulation

CoRL 2024poster

The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects. Observing the generalization and reasoning capabilities of Multimodal Large Language Models (MLLMs), previous approaches have aimed to utilize these models to enhance robotic syst…

Cited by 4SourceScholar