5 papers · 1 filter
Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
Jiashu Yao, Heyan Huang, Daiqing Wu +2
To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifur…
Incorporating Self-Rewriting into Large Language Model Reasoning Reinforcement
Jiashu Yao, Heyan Huang, Shuang Zeng +6
Through reinforcement learning (RL) with outcome correctness rewards, large reasoning models (LRMs) with scaled inference computation have demonstrated substantial success on compl…
MME-SCI: A Comprehensive and Challenging Science Benchmark for Multimodal Large Language Models
Jiacheng Ruan, Dan Jiang, Xian Gao +3
Recently, multimodal large language models (MLLMs) have achieved significant advancements across various domains, and corresponding evaluation benchmarks have been continuously ref…
ListConRanker: A Contrastive Text Reranker with Listwise Encoding
Junlong Liu, Yue Ma, Ruihui Zhao +3
Reranker models aim to re-rank the passages based on the semantics similarity between the given query and passages, which have recently received more attention due to the wide appl…
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection
Cilin Yan, Jingyun Wang, Lin Zhang +6
Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prom…