Master Mathématiques et Applications Sorbonne Université 2025
In order to succeed in these domains, an. LLM needs to make a sequence of intelligent decisions over multiple turns instead of generating the most probable text.
Training Language Model Agents via Hierarchical Multi-Turn RLAfter pre-training and fine- tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general. HiAgent: Hierarchical Working Memory Management for Solving ...Abstract. Interactive multimodal agents must convert raw visual ob- servations into coherent sequences of language-conditioned. Understanding Self-Evolution in LLM Agents via Multi-Turn ... - RAGENThrough policy gradient optimiza- tion driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other ...
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