6 papers
EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
Yongrui Heng, Chaoya Jiang, Han Yang +2
Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degradation as model predictions…
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
Bo Li, Mingda Wang, Gexiang Fang +2
We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retri…
Instruction Data Selection via Answer Divergence
Bo Li, Mingda Wang, Shikun Zhang +1
Instruction tuning relies on large instruction-response corpora whose quality and composition strongly affect downstream performance. We propose Answer Divergence-Guided Selection…
Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories
Peiyang Liu, Zhirui Chen, Xi Wang +4
Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain inefficient. Standard approaches ret…
Reverse-Engineered Reasoning for Open-Ended Generation
Haozhe Wang, Haoran Que, Qixin Xu +9
While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical…