3 citations · 4 across the 28 of their papers we have counts for
33 papers
MAS-OPD: On-Policy Distillation for Multi-agent Systems
Qiyong Zhong, Mao Zheng, Mingyang Song +5
Multi-agent systems (MAS) split a task across specialized roles and are promising on complex tasks, yet a prevailing approach relies on inference-time orchestration alone. General-…
USA: Update-aware SAM for Cross-domain On-Policy Disitllation of Language Agents
Qiyong Zhong, Mao Zheng, Mingyang Song +6
On-policy distillation instils multi-turn agentic reasoning through dense token-level supervision on the student's own trajectories, but a single domain saturates early, so further…
RAZOR: Pruning Replaceable Experts in LLMs
Mingyang Song, Mao Zheng
Mixture-of-experts (MoE) models activate only a few experts per token but store the entire expert pool. Pruning this pool requires identifying experts whose removal preserves model…
Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Zheng Zhang, Liu Liu, Qi Chai +4
Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a f…
Distill What You Trust: Reliability-Aware Multi-Teacher On-Policy Distillation
Jie Sun, Mao Zheng, Mingyang Song +8
Multi-teacher on-policy distillation allows a student to learn from complementary specialists on its own trajectories. Domain-routed approaches, however, select one teacher per exa…
Data-Free On-Policy Distillation: How Far Can We Go Without External Data?
Gengsheng Li, Mao Zheng, Mingyang Song +10
On-policy distillation (OPD) is increasingly applied to frontier foundation model post-training. Prior work in this area has largely focused on algorithmic advances, yet it remains…
Simple-OPD: Demystifying Warm-up for On-policy Distillation
Tao Liu, Taiqiang Wu, Mao Zheng +5
On-policy distillation (OPD) trains a student on its own rollouts with token-level supervision from teacher models, but its effectiveness can depend strongly on the warm-up stage b…
NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generation
Guo Chen, Ziwen Li, Maolin Zheng +3
Retrieval-Augmented Generation (RAG) significantly enhances the ability of Large Language Models (LLMs) to provide accurate and contextually relevant answers by dynamically integra…
EasyOPD: An Easy-to-use On-Policy Distillation Framework for Large Language Models
Jie Sun, Mao Zheng, Mingyang Song +7
Conventional language-model distillation often relies on fixed teacher-generated data, which may not cover the states encountered by an evolving student policy. On-policy distillat…
On-Policy Distillation with Curriculum Turn-level Guidance for Multi-turn Agents
Gengsheng Li, Mao Zheng, Mingyang Song +8
Multi-turn agents that plan, invoke tools, and interact with environments offer a promising paradigm for solving complex tasks, yet their capabilities typically rely on very large…
Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents
Tianxiang Fei, Mingyang Song, Mao Zheng +1
Long-term memory for an LLM agent is more than retrieving the right passage at the right time. Current memory systems collapse belief revision, causal coupling, and cross-domain ab…
HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domains
Zheng Li, Mao Zheng, Mingyang Song +1
General-purpose machine translation benchmarks such as FLORES-200 have reached a saturation regime on Chinese-English pairs, where modern large language models cluster within a nar…
IFMTBench: A Comprehensive Benchmark for Multilingual Translation Instruction Following
Mingrui Sun, Mao Zheng, Zheng Li +1
Modern translation workflows demand more than semantic equivalence. Users routinely require models to preserve JSON or HTML schemas, honor curated glossaries, disambiguate with pro…
SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation
Jie Sun, Mao Zheng, Mingyang Song +6
On-policy distillation (OPD) is a standard tool for transferring teacher behavior to a smaller student, but it implicitly assumes that teacher and student predictions are comparabl…
Rubric-based On-policy Distillation
Junfeng Fang, Zhepei Hong, Mao Zheng +7
On-policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white-box scenarios. We contend that struct…
SOD: Step-wise On-policy Distillation for Small Language Model Agents
Qiyong Zhong, Mao Zheng, Mingyang Song +5
Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While reinforcement…
Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
Gengsheng Li, Tianyu Yang, Junfeng Fang +6
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is wid…
PRISM: Probability Reallocation with In-Span Masking for Knowledge-Sensitive Alignment
Chenning Xu, Mao Zheng, Mingyang Song
Supervised fine-tuning (SFT) with token-level hard labels can amplify overconfident imitation of factually unsupported targets, causing hallucinations that propagate in multi-sente…
A Survey of On-Policy Distillation for Large Language Models
Mingyang Song, Mao Zheng
On-Policy Distillation (OPD) trains large language models with teacher guidance on student-generated trajectories, addressing the mismatch between fixed demonstrations and the hist…
Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
Mingyang Song, Mao Zheng
Model merging combines the parameters of multiple neural networks into a single model without additional training. As fine-tuned large language models (LLMs) proliferate, merging o…
Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge
Mingyang Song, Mao Zheng, Chenning Xu
The paradigm of LLM-as-a-judge relies on a critical assumption, namely that high inter-evaluator agreement indicates reliable and objective evaluation. We present two complementary…
CodeDelegator: Mitigating Context Pollution via Role Separation in Code-as-Action Agents
Tianxiang Fei, Cheng Chen, Yue Pan +2
Recent advances in large language models (LLMs) allow agents to represent actions as executable code, offering greater expressivity than traditional tool-calling. However, real-wor…
PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation
Chenning Xu, Mao Zheng, Mingyu Zheng +1
Podcast script generation requires LLMs to synthesize structured, context-grounded dialogue from diverse inputs, yet systematic evaluation resources for this task remain limited. T…
Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning
Mingyang Song, Mao Zheng
As test-time scaling becomes a pivotal research frontier in Large Language Models (LLMs) development, contemporary and advanced post-training methodologies increasingly focus on ex…