9 papers
TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models
Shuning Wang, Zhiheng Wu, Xun Zhou +8
Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual conditions can reinforce share…
See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding
Shuning Wang, Zhiheng Wu, YiNuo Lu +6
Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks. However, existing methods still face two key limitations: ev…
Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
Bowen Ping, Chengyou Jia, Minnan Luo +2
Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, mode…
GDEPO: Group Dual-dynamic and Equal-right Advantage Policy Optimization for Sample-Efficient Reinforcement Learning
Zhengqing Yan, Xinyang Liu, Yi Zhang +7
Automated Theorem Proving (ATP) is a fundamental AI challenge, requiring machine-verifiable proofs in formal languages like Lean to evaluate reasoning. Reinforcement Learning (RL),…
Bootstrapping LLMs via Preference-Based Policy Optimization
Chen Jia
Bootstrapping large language models (LLMs) through preference-based policy optimization offers a promising direction for aligning model behavior with human preferences without rely…
A Survey on Large Language Models for Mathematical Reasoning
Peng-Yuan Wang, Tian-Shuo Liu, Chenyang Wang +8
Mathematical reasoning has long represented one of the most fundamental and challenging frontiers in artificial intelligence research. In recent years, large language models (LLMs)…
Online Knowledge Distillation with Reward Guidance
Chen Jia
This work studies knowledge distillation (KD) for large language models (LLMs) through preference optimization. We propose a reward-guided imitation learning framework for sequenti…
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity
T. Y. S. S. Santosh, Chen Jia, Patrick Goroncy +1
This paper addresses the task of legal summarization, which involves distilling complex legal documents into concise, coherent summaries. Current approaches often struggle with con…
Adversarial Moment-Matching Distillation of Large Language Models
Chen Jia
Knowledge distillation (KD) has been shown to be highly effective in guiding a student model with a larger teacher model and achieving practical benefits in improving the computati…