Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning

• URL: http://arxiv.org/abs/2605.09009v1
• Date: Sat, 09 May 2026 15:49:49 GMT
• Title: Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
• Abstract summary: In this paper, we study the ICL capabilities of large language models (LLMs) in sequential decision-making settings.<n>We fine-tune pretrained LLMs to perform few-shot decision-making directly from offline, oracle-labeled trajectories.<n>Our framework enables flexible imitation of policies through supervised fine-tuning.
• Score: 0.7340845393655052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains underexplored. In this paper, we study the ICL capabilities of LLMs in sequential decision-making settings, including Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Ambiguous POMDPs (APOMDPs). We fine-tune pretrained LLMs to perform few-shot decision-making directly from offline, oracle-labeled trajectories. Our framework enables flexible imitation of policies through supervised fine-tuning (SFT). Theoretically, we focus on linear MDPs and interpret a fine-tuned attention layer as implicitly estimating optimal Q-functions from in-context data. Building on this interpretation, we derive an end-to-end suboptimality bound for the induced policy that separates the in-context estimation error from the training-length bias. Empirically, across synthetic MDP, POMDP, and APOMDP settings, we find that fine-tuned LLMs achieve substantially smaller optimality gaps than in-context-only and random baselines, with especially large gains in longer-horizon, partially observed, and model-ambiguous environments. Together, these results show that supervised fine-tuning provides an effective route to endowing pretrained LLMs with sequential decision-making capabilities from offline data, which is an important advantage in domains such as healthcare where offline data are abundant.

Related papers

• Goal-Conditioned Supervised Learning for LLM Fine-Tuning [10.43951412455864]
  We propose goal-conditioned supervised learning as an offline fine-tuning framework for large language models.<n>Our core idea is to treat feedback signals directly as an explicit goal and train the model, purely through supervised learning, to generate responses that achieve that goal.
  arXiv  Detail & Related papers  (2026-05-08T01:55:40Z)
• Pushing LLMs to Their Logical Reasoning Bound: The Role of Data Reasoning Intensity [59.27594125465172]
  We introduce Data Reasoning Intensity (DRI), a novel metric that quantifies the latent logical reasoning complexity of samples.<n>We then introduce a re-cognizing optimization strategy that systematically enhances the logical reasoning intensity of training data.
  arXiv  Detail & Related papers  (2025-09-29T14:20:04Z)
• Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets [0.0]
  Large Language Models (LLMs) can perform predictive tasks over structured inputs without explicit fine-tuning on downstream tasks.<n>We investigate the empirical function approximation capability of LLMs on small-scale structured datasets for classification, regression and clustering tasks.<n>Our findings suggest that LLMs can serve as general-purpose predictive engines for structured data, with clear strengths in classification and significant limitations in regression and clustering.
  arXiv  Detail & Related papers  (2025-08-24T15:00:51Z)
• Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs [57.82819770709032]
  Large language models (LLMs) can be effective context-aided forecasters via na"ive direct prompting.<n>ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context.<n>CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines.<n> IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models.
  arXiv  Detail & Related papers  (2025-08-13T16:02:55Z)
• Feedback-Induced Performance Decline in LLM-Based Decision-Making [6.5990946334144756]
  Large Language Models (LLMs) can extract context from natural language problem descriptions.<n>This paper studies the behaviour of these models within a Markov Decision Process (MDPs)
  arXiv  Detail & Related papers  (2025-07-20T10:38:56Z)
• Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining [75.14823970163685]
  We propose a novel preference learning framework, Modality-Balancing Preference Optimization (MBPO), to address the modality imbalance in LMMs.<n>MBPO constructs a more effective offline preference dataset by generating hard negatives, i.e., rejected responses misled by LLM biases.<n>It can enhance LMM performance on challenging vision-language tasks and effectively reduce hallucinations.
  arXiv  Detail & Related papers  (2025-05-20T03:59:05Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Embodied CoT Distillation From LLM To Off-the-shelf Agents [6.318203525449058]
  DeDer is a framework for decomposing and distilling the embodied reasoning capabilities from large language models (LLMs)<n>Our experiments with the ALFRED benchmark demonstrate that DeDer surpasses leading language planning and distillation approaches.
  arXiv  Detail & Related papers  (2024-12-16T07:18:02Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities [0.35998666903987897]
  This report examines the fine-tuning of Large Language Models (LLMs) It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. The report introduces a structured seven-stage pipeline for fine-tuning LLMs.
  arXiv  Detail & Related papers  (2024-08-23T14:48:02Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.