Tag: Reasoning
All the articles with the tag "Reasoning".
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SEM: Reinforcement Learning for Search-Efficient Large Language Models
本文提出 *SEM* 框架,通过强化学习优化大型语言模型的搜索行为,在减少冗余搜索的同时提升回答准确性,显著提高推理效率。
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Large Language Models Think Too Fast To Explore Effectively
本文通过《Little Alchemy 2》游戏评估大型语言模型(LLMs)的探索能力,发现大多数LLMs因过早决策和过度依赖不确定性驱动策略而表现不如人类,但o1和DeepSeek-R1通过平衡赋能和深入推理显著超越人类,揭示了推理深度和架构设计对开放性探索的重要性。
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CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models
本文提出CCSK框架,通过Siamese Network和Response Quality Model动态融合查询相似性和响应质量,优化大型语言模型的信息检索决策,在多个问答数据集上显著提升了F1分数和准确率。
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Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards
REWARD-SQL introduces a framework for Text-to-SQL by decomposing queries into Chain-of-CTEs and using Process Reward Models (PRMs) with GRPO and Best-of-N sampling, achieving a state-of-the-art 68.9% execution accuracy on the BIRD dataset with a 7B model.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
本文提出PLAN-AND-ACT框架,通过分离规划和执行模块、利用合成数据训练和动态重规划,提高LLM代理在复杂长期任务中的性能,并在web导航基准上达到state-of-the-art结果。