15 citations · 57 across the 23 of their papers we have counts for
27 papers
LeAct: Learning to Reason from Expert Actions
Ziran Yang, Chengshuai Shi, Raj Ghugare +3
Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely untapped source of supervision l…
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
Zihan Chen, Songwei Dong, Chengshuai Shi +4
Sequentially evolving LLM memory enables agents to reuse past experience, but existing systems usually deploy each locally generated memory update without checking whether it impro…
Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
Songwei Dong, Zihan Chen, Chengshuai Shi +3
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluation…
Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits
Donghao Li, Chengshuai Shi, Weijuan Ou +2
Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently identifying the most effective…
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Seth Karten, Joel Zhang, Tersoo Upaa +5
Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agents' long-horizon partial-obser…
-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses
Di Wu, Chengshuai Shi, Jing Yang +1
Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large language models. While most existing approaches rely on the reverse KL-…