arXiv:2511.04393v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in interactive and dynamic environments. Yet, since they were not originally designed for DM, recent studies show that LLMs can struggle even in basic online DM problems, failing to achieve low regret or an effective exploration-exploitation tradeoff. To address this, we introduce Iterative Regret-Minimization Fine-Tuning (Iterative RMFT), a post-training procedure that repeatedly distills low-regret decision trajectories back into the base model. At e
The paper addresses a critical current limitation of LLMs as agents, proposing a novel post-training solution as their deployment in decision-making roles accelerates.
This development could significantly enhance the reliability and effectiveness of LLMs in autonomous decision-making scenarios, broadening their practical application and impact across industries.
LLMs, previously limited by poor decision-making and exploration-exploitation tradeoffs, can now be systematically improved during a post-training phase to exhibit lower regret and more 'intelligent' agentic behavior.
- · AI developers and researchers
- · Companies deploying AI agents
- · SaaS companies leveraging LLM agents
- · Traditional algorithmic control systems
- · LLM developers without advanced fine-tuning strategies
More robust and efficient AI agents become deployable across various sectors, automating complex tasks.
Increased trust and adoption of AI-driven autonomous systems in critical applications and workflows.
The acceleration of 'AI agents' narrative potentially leads to faster collapse of white-collar workflows and new SaaS layers being rebuilt on agentic foundations.
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Read at arXiv cs.AI