7 citations · 21 across the 16 of their papers we have counts for
20 papers
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation
Bingxi Hou, Guochao Jiang, Guofeng Quan +4
Policy-shift distillation reuses the difference between a teacher's log probabilities before and after reinforcement learning (RL) as supervision on student-generated trajectories.…
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…
DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning
Guochao Jiang, Jingyi Song, Guofeng Quan +3
Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative Policy Optimization offers an…
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…
FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization
Haiyang Xiao, Weiqing Li, Jinyue Guo +3
Although post-training quantization (PTQ) provides an efficient numerical compression scheme for deploying large language models (LLMs) on resource-constrained devices, the represe…
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…
FlashThink: An Early Exit Method For Efficient Reasoning
Guochao Jiang, Guofeng Quan, Zepeng Ding +3
Large Language Models (LLMs) have shown impressive performance in reasoning tasks. However, LLMs tend to generate excessively long reasoning content, leading to significant computa…
RLAP: A Reinforcement Learning Enhanced Adaptive Planning Framework for Multi-step NLP Task Solving
Zepeng Ding, Dixuan Wang, Ziqin Luo +3
Multi-step planning has been widely employed to enhance the performance of large language models (LLMs) on downstream natural language processing (NLP) tasks, which decomposes the…
FlowKV: A Disaggregated Inference Framework with Low-Latency KV Cache Transfer and Load-Aware Scheduling
Weiqing Li, Guochao Jiang, Xiangyong Ding +5
Disaggregated inference has become an essential framework that separates the prefill (P) and decode (D) stages in large language model inference to improve throughput. However, the…
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…
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
Guochao Jiang, Ziqin Luo, Chengwei Hu +2
Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared…
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…
AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
Yuchen Shi, Guochao Jiang, Tian Qiu +1
The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the…
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
Guochao Jiang, Zepeng Ding, Yuchen Shi +1
In recent years, the rise of large language models (LLMs) has made it possible to directly achieve named entity recognition (NER) without any demonstration samples or only using a…
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Zepeng Ding, Ruiyang Ke, Wenhao Huang +4
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex…
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Ziqin Luo, Haixia Han, Haokun Zhao +6
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries. These methods tend to consider token…
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Dixuan Wang, Yanda Li, Junyuan Jiang +5
Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate re…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Yanda Li, Dixuan Wang, Jiaqing Liang +4
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.…
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
Guochao Jiang, Ziqin Luo, Yuchen Shi +3
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has…