9 papers
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
Zheye Deng, Sha Li, Changlong Yu +4
Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and revision…
Process Reward Informed Tree Rollout for Effective Multi-Turn RL
Xintong Li, Sha Li, Yuwei Zhang +8
Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories fo…
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
Nikhil Abhyankar, Sha Li, Sanchit Kabra +3
Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains. Existing approaches cast disco…
Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation
Yuwei Zhang, Sha Li, Changlong Yu +9
Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy self-distillation offers a pr…
: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
Quyet V. Do, Thinh Pham, Nguyen Nguyen +3
We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, , constructs high-quality reasoning data through ri…
Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
Sha Li, Naren Ramakrishnan
Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning.…
Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning
Xintong Li, Sha Li, Rongmei Lin +10
Large reasoning models improve with more test-time computation, but often overthink, producing unnecessarily long chains-of-thought that raise cost without improving accuracy. Prio…
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
Fengran Mo, Yifan Gao, Sha Li +7
Large Language Models (LLMs) have become a popular interface for human-AI interaction, supporting information seeking and task assistance through natural, multi-turn dialogue. To r…
WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance
Genglin Liu, Shijie Geng, Sha Li +4
Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains. Howev…