Token-Budget-Aware LLM Reasoning
Tingxu Han, Chunrong Fang, Shiyu Zhao +3 authors
A token-budget-aware framework dynamically estimates and allocates token budgets for LLM reasoning, reducing costs with minimal performance loss.
Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
Jian Hu, Xibin Wu, Weixun Wang +3 authors
OpenRLHF is an open-source RLHF framework that enhances training efficiency and accessibility for researchers and practitioners.
Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval
Zelong Sun, Jun Wang, Kaicheng Yang +3 authors
UniME-R1 improves multimodal retrieval by generating retrieval-centric reasoning guided by initial candidate feedback rather than query-only explanations.
Contrastive Decoding Improves Reasoning in Large Language Models
Sean O'Brien, Mike Lewis
Contrastive Decoding, a simple and training-free text generation method, outperforms other decoding techniques on various reasoning tasks and logical benchmarks by improving perceived quality and reducing reasoning errors.
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez +7 authors
Chain-of-thought prompting enhances large language model performance primarily in math or logic tasks by improving symbolic reasoning, but it is less beneficial in other domains and can be selectively applied to save inference costs.
How FaR Are Large Language Models From Agents with Theory-of-Mind?
Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju +9 authors
LLMs perform well in inferring beliefs but struggle translating these into actions; Foresee and Reflect (FaR) improves their action guidance in social scenarios.
SHAPE of Chain-of-Thought in Math Reasoning
Jonghyun Song, Sangjun Song, Minjae Oh +3 authors
SHAPE analyzes chain-of-thought reasoning via semantic spaces and heuristics to diagnose LLM mathematical reasoning and improve post-training.
Stay on topic with Classifier-Free Guidance
Guillaume Sanchez, Honglu Fan, Alexander Spangher +3 authors
Classifier-Free Guidance enhances performance across various language modeling tasks and improves the faithfulness and coherence of AI assistants, outperforming models with higher parameter counts.
Measuring Faithfulness in Chain-of-Thought Reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan +27 authors
Larger language models may not produce faithful reasoning when using chain-of-thought despite performance improvements, depending on model size and task.
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