16 papers
Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
Yuanjian Xu, Jianing Hao, Tianze Sun +7
Data strategy plays a central role in training large language models (LLMs). It governs the selection, weighting, and scheduling of training data, thereby determining the capabilit…
AgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations
Yuanjian Xu, Jinyi Han, Jianing Hao +5
Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for jointly characterizing potentia…
Skill-Use: Can LLMs Actually Use Skills in Agentic Harnesses?
Jinyi Han, Yuanjian Xu, Ying Liao +6
Large language model (LLM) agents increasingly rely on skills, structured documents that specify when to act, which procedure to follow, and which tools are allowed. Existing evalu…
From Outcomes to Actions: Leveraging Hindsight for Long-Horizon Language Agent Training
Zishang Jiang, Tingyun Li, Jinyi Han +7
Reinforcement learning (RL) has become a widely adopted technique for improving large language models (LLMs) on complex tasks. Despite this progress, existing RL methods still face…
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models
Tingyun Li, Zishang Jiang, Jinyi Han +8
Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-orient…
GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)
Jiaqing Liang, Jinyi Han, Weijia Li +15
Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memories, and raw environmental fee…
Not All Negative Samples Are Equal: LLMs Learn Better from Plausible Reasoning
Zixiang Di, Jinyi Han, Shuo Zhang +8
Learning from negative samples holds great promise for improving Large Language Model (LLM) reasoning capability, yet existing methods treat all incorrect responses as equally info…
Structured Reasoning for Large Language Models
Jinyi Han, Zixiang Di, Zishang Jiang +4
Large language models (LLMs) achieve strong performance by generating long chains of thought, but longer traces always introduce redundant or ineffective reasoning steps. One typic…
Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts
Xinyi Wang, Jinyi Han, Zishang Jiang +7
Reinforcement Learning (RL) has become a key driver for enhancing the long chain-of-thought (CoT) reasoning capabilities of Large Language Models (LLMs). However, prevalent methods…
Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs
Zishang Jiang, Jinyi Han, Tingyun Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Models (LLMs). However, the effect…
Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Jinyi Han, Ying Huang, Ying Liao +11
Large Reasoning Models (LRMs) have achieved impressive performance on challenging tasks, yet their deep reasoning often incurs substantial computational costs. To achieve efficient…
Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
Jinyi Han, Tingyun Li, Shisong Chen +8
While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assi…
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Jinyi Han, Xinyi Wang, Haiquan Zhao +9
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most exi…
CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation
Cairong Yan, Jinyi Han, Jin Ju +3
Clustering bandits have gained significant attention in recommender systems by leveraging collaborative information from neighboring users to better capture target user preferences…
CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theory
Haokun Zhao, Jinyi Han, Jiaqing Liang +3
Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improve…
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
Chengyu Du, Jinyi Han, Yizhou Ying +9
Recent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful ou…