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cs.LG2025
AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin
Jian Xiong, Jingbo Zhou, Jingyong Ye +2
Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models (LLMs), especially in scenarios where supervised…
cs.LG2024
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
Ji Liu, Jiaxiang Ren, Ruoming Jin +4
As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large Language Models (LLMs). While LLMs corr…