5 papers · 1 filter
Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning
Haoran Zhao, Wei Du, Dingwen Yang +7
Producing task-specific large language models requires discovering effective training strategies through experimentation. Automated fine-tuning systems have made this experimentati…
Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization
Junzhe Wang, Zhiheng Xi, Yajie Yang +4
Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavailable during pretraining. Whil…
Positive-Only Drifting Policy Optimization
Qi Zhang
In the field of online reinforcement learning (RL), traditional Gaussian policies and flow-based methods are often constrained by their unimodal expressiveness, complex gradient cl…
Step-level Denoising-time Diffusion Alignment with Multiple Objectives
Qi Zhang, Dawei Wang, Shaofeng Zou
Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences, typically by optimizing a single reward function under a KL regular…
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
Mingqi Wu, Zhihao Zhang, Qiaole Dong +11
Reasoning in large language models has long been a central research focus, and recent studies employing reinforcement learning (RL) have introduced diverse methods that yield subst…