Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
arXiv:2606.19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization. Existing adaptive curriculum learning methods typically prioritize prompts of intermediate difficulty, treating problem selection as a standard bandit problem with independent arms and overlooking the structured, heterogeneous nature of the task space. In this work, we frame problem sampling as a manifold-structured bandit problem wit
The paper addresses a critical challenge in current AI development — the efficiency and effectiveness of training large language models, especially as they become more complex and their reasoning capabilities are emphasized.
Improving the training efficiency and reasoning capabilities of LLMs through sophisticated curriculum learning directly impacts the rate of AI advancement and the performance ceiling of future AI systems, including AI agents.
This research could lead to more robust and capable LLMs trained with fewer resources, accelerating the development of advanced AI applications, particularly those requiring complex reasoning.
- · AI research institutions
- · LLM developers
- · AI-powered product companies
- · Inefficient AI training methodologies
More efficient and powerful LLMs will accelerate AI development and deployment across various sectors.
Reduced computational costs for achieving higher-performing AI systems could lower barriers to entry for some AI development.
Enhanced reasoning capabilities in LLMs could lead to breakthroughs in autonomous AI agents and more sophisticated automated decision-making systems across industries.
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Read at arXiv cs.CL