46 citations · 156 across the 24 of their papers we have counts for
24 papers
PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning
Chunji Lv, Yangguang Wei, Junlin Liu +6
Large language model agents have shown strong potential in complex interactive tasks, yet their reinforcement learning (RL) is often hindered by sparse rewards, as a long multi-tur…
ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents
Xingjian Wu, Xuhang Zhu, Xingchen Liu +6
As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish…
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Xingjian Wu, Junlin Liu, Xingchen Liu +6
Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD). Whil…
DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space
Jiangwang Chen, Zixin Song, Junlin Liu +10
Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable…
From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search
Junlin Liu, Jiangwang Chen, Zixin Song +7
Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforc…
Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
Kailin Jiang, Lei Liu, Jian Xi +7
As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evalua…
PReM: Learning What to Preserve and When to Refresh for Context Compression
Bohan Yu, Lei Shen, Chenxi Zhou +5
Efficient long-context inference is not only about reducing memory cost, but also about keeping useful contextual evidence accessible as generation proceeds. However, existing comp…
Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill
Tao Chen, Gangwei Jiang, Pengyu Cheng +10
Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current rew…
Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
Chengwei Li, Junlin Liu, Yang Gao
As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aerial vehicles (UCAVs) faces s…
Gradient Starvation in Binary-Reward GRPO: Why Group-Mean Centering Fails and Why the Simplest Fix Works
Wenhua Nie, Jianan Wu, Junlin Liu +6
Group Relative Policy Optimization (GRPO) is a standard algorithm for reinforcement learning from verifiable rewards, but its group-mean-centered advantage can fail under binary re…
The Coupling Tax: How Shared Token Budgets Undermine Visible Chain-of-Thought Under Fixed Output Limits
Wenhua Nie, Junlin Liu, Jianan Wu +5
Chain-of-thought reasoning is often treated as a monotone way to improve language-model accuracy by letting a model think longer. We identify a countervailing effect, the coupling…
General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks
Junlin Liu, Shengnan An, Shuang Zhou +10
Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics and physics. However, their abil…
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions
Shengnan An, Xunliang Cai, Xuezhi Cao +8
We present AMO-Bench, an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty, comprising 50 human-crafted problems. Existing benchmarks have wid…
Deep Reinforcement Learning in Factor Investment
Junlin Liu
Deep reinforcement learning has shown promise in trade execution, yet its use in low-frequency factor portfolio construction remains under-explored. A key obstacle is the high-dime…
A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
Xiaohua Feng, Yuyuan Li, Chengye Wang +3
Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpre…
ContextModule: Improving Code Completion via Repository-level Contextual Information
Zhanming Guan, Junlin Liu, Jierui Liu +7
Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by predicting and generating new code in real-time. Ho…
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Peilin Zhou, Meng Cao, You-Liang Huang +6
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visu…
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Hongjian Zhou, Fenglin Liu, Boyang Gu +16
Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a…
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
Junling Liu, Ziming Wang, Qichen Ye +3
Large Language Models (LLMs) have introduced a new era of proficiency in comprehending complex healthcare and biomedical topics. However, there is a noticeable lack of models in la…
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
Qichen Ye, Junling Liu, Dading Chong +8
Integrating large language models (LLMs) into healthcare holds great potential but faces challenges. Pre-training LLMs from scratch for domains like medicine is resource-heavy and…
LLMRec: Benchmarking Large Language Models on Recommendation Task
Junling Liu, Chao Liu, Peilin Zhou +8
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However,…
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
Junling Liu, Peilin Zhou, Yining Hua +8
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the l…
Is ChatGPT a Good Recommender? A Preliminary Study
Junling Liu, Chao Liu, Peilin Zhou +3
Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific a…
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
Yuyuan Li, Xiaolin Zheng, Chaochao Chen +1
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the…