1 citations · 1 across the 6 of their papers we have counts for
9 papers
Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability
Bingxi Hou, Guochao Jiang, Guofeng Quan +4
On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student predictions requires alignment…
Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training
Tingyun Li, Wenfeng Feng, Weiqing Li +3
Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Autonomous systems automate par…
Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search
Chuzhan Hao, Wenfeng Feng, Guochao Jiang +3
Reinforcement learning (RL) has become an effective approach for advancing the reasoning capabilities of large language models (LLMs) through the strategic integration of external…
VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models
Guochao Jiang, Wenfeng Feng, Guofeng Quan +4
Policy-based reinforcement learning currently plays an important role in improving LLMs on mathematical reasoning tasks. However, existing rollout-based reinforcement learning meth…
PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning
Wenfeng Feng, Penghong Zhao, Guochao Jiang +4
Critic-free reinforcement learning methods, particularly group policies, have attracted considerable attention for their efficiency in complex tasks. However, these methods rely he…
DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Chuzhan Hao, Wenfeng Feng, Yuewei Zhang +1
Multi-step agentic retrieval systems based on large language models (LLMs) have demonstrated remarkable performance in complex information search tasks. However, these systems stil…
RASD: Retrieval-Augmented Speculative Decoding
Guofeng Quan, Wenfeng Feng, Chuzhan Hao +3
Speculative decoding accelerates inference in large language models (LLMs) by generating draft tokens for target model verification. Current approaches for obtaining draft tokens r…
AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation
Wenfeng Feng, Chuzhan Hao, Yuewei Zhang +3
Leveraging the autonomous decision-making capabilities of large language models (LLMs) has demonstrated superior performance in reasoning tasks. However, despite the success of ite…
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models
Wenfeng Feng, Chuzhan Hao, Yuewei Zhang +2
Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models (LLMs). However, achieving the right balance of data is crucial to preve…