Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs

📰 ArXiv cs.AI

Learn how to improve sequential information gathering in LLMs using Amortising Bayesian Experimental Design, a fine-tuning approach for better decision-making

advanced Published 7 Jul 2026

Action Steps

Implement Amortised Sequential Information Gathering (ASIG) using a multi-turn extension of Group Relative Policy Optimisation
Fine-tune LLM policies with ASIG to improve sequential decision-making
Apply Bayesian Experimental Design (BED) to amortise the information gathering process
Evaluate the performance of ASIG in sequential information gathering tasks
Compare the results with other fine-tuning approaches to assess the effectiveness of ASIG

Who Needs to Know This

Researchers and engineers working with LLMs can benefit from this approach to enhance their models' ability to gather information effectively in multi-turn interactions

Key Insight

💡 Amortising Bayesian Experimental Design can enhance LLMs' ability to gather information effectively in multi-turn interactions

Key Takeaways

Learn how to improve sequential information gathering in LLMs using Amortising Bayesian Experimental Design, a fine-tuning approach for better decision-making

Full Article

Title: Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs

Abstract:
arXiv:2607.03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings. We introduce Amortised Sequential Information Gathering (ASIG), a fine-tuning approach that amortises Bayesian Experimental Design (BED) into LLM policies via a multi-turn extension of Group Relative Policy Optimisation with

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