Intrinsic Credit Assignment for Long Horizon Interaction

2026-02-16 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

A new method called ΔBelief-RL has been proposed to train agents for long-horizon interactions under uncertainty. This approach utilizes a language model's intrinsic beliefs to assign credit for intermediate progress, specifically by measuring the change in the probability an agent assigns to a target solution. Training on synthetic interaction data, ΔBelief-RL fosters information-seeking capabilities that consistently surpass purely outcome-based reward systems in Reinforcement Learning. The method demonstrates improved generalization to out-of-distribution applications, including customer service and personalization. Furthermore, its performance scales positively with increased test-time interactions beyond the training horizon, enhancing interaction-efficiency even on Pass@k metrics, offering a scalable strategy for long-horizon uncertainty navigation.

Key takeaway

For research scientists developing agents for complex, long-horizon tasks, consider integrating ΔBelief-RL's intrinsic reward mechanism. This approach can significantly improve an agent's ability to navigate uncertainty and enhance information-seeking behaviors, leading to better performance and generalization across diverse applications. Your models could achieve greater interaction-efficiency and scale effectively beyond initial training horizons.

Key insights

ΔBelief-RL uses a language model's intrinsic belief changes to reward intermediate progress in long-horizon tasks.

Principles

• Intrinsic beliefs can guide credit assignment.
• Information-seeking improves long-horizon performance.

Method

ΔBelief-RL trains agents using rewards derived from the change in a language model's probability assignment to the target solution, enabling credit assignment for intermediate actions.

In practice

• Apply ΔBelief-RL to customer service agents.
• Use for personalization systems.

Topics

• ΔBelief-RL
• Reinforcement Learning
• Language Models
• Credit Assignment
• Long-Horizon Interaction

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.