Iterative RMFT: Post-Training LLMs for Better Decision-Making via Regret Minimization
A novel technique named Iterative Regret-Minimization Fine-Tuning (Iterative RMFT) has been developed by researchers to improve large language models (LLMs) for decision-making tasks. This post-training method involves the repeated extraction of low-regret decision paths into the foundational model. In each round, the model generates several trajectories, identifies the k trajectories with the least regret, and refines itself using these selections. Unlike previous methods that depend on predefined algorithms or manually designed reasoning templates, Iterative RMFT utilizes the regret metric to draw out the model's intrinsic reasoning capabilities. The research paper can be accessed on arXiv with the ID 2511.04393.
Key facts
- Iterative RMFT is a post-training procedure for LLMs.
- It repeatedly distills low-regret decision trajectories into the base model.
- At each iteration, the model selects k-lowest regret trajectories for fine-tuning.
- The method uses the regret metric to elicit the model's own reasoning.
- It differs from prior methods that distill action sequences from known DM algorithms.
- It also differs from methods relying on manually crafted chain-of-thought templates.
- The paper is published on arXiv with ID 2511.04393.
- The approach aims to improve LLMs' decision-making in interactive environments.
Entities
Institutions
- arXiv