Tag: Multi-Agent
All the articles with the tag "Multi-Agent".
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Agentic AI: The Era of Semantic Decoding
本文提出语义解码视角,将大型语言模型、人类和工具的协作框架化为语义空间中的优化过程,通过语义令牌的交换和语义解码算法的设计探索AI系统的新计算范式。
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When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator
This paper demonstrates that a 1.5B parameter reasoning model (Distill-R1) outperforms larger non-reasoning LLMs as a discriminator in a text-to-SQL planning framework by leveraging a novel soft score extraction method from chain-of-thought outputs, though it struggles significantly as a generator.
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RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
本文提出StarPO框架和RAGEN系统,通过多轮轨迹级别强化学习训练LLM智能体,揭示了训练不稳定性(如Echo Trap)和推理能力不足的挑战,并通过StarPO-S改进稳定性和泛化性,但推理能力仍需细粒度奖励设计支持。
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EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuning
本文提出EMORL框架,通过集成学习分别训练单目标模型并在隐藏状态层聚合,结合分层网格搜索优化权重,在咨询反思生成任务中实现了与传统方法相当的性能,同时显著提升了训练效率、可扩展性和解释性。
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Communicating Activations Between Language Model Agents
This paper introduces Activation Communication (AC), a novel method for inter-LLM communication using intermediate activations instead of natural language, achieving up to 27% performance improvement over traditional methods with significantly reduced compute across coordination games and reasoning benchmarks.