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[2602.12342] Intrinsic Credit Assignment for Long Horizon Interaction
Summary
This article presents a novel approach, {}Belief-RL, for training agents to navigate uncertainty over long horizons by utilizing intrinsic rewards for intermediate progress.
Why It Matters
Understanding how to effectively assign credit in reinforcement learning is crucial for developing AI systems that can perform well in complex, uncertain environments. This research introduces a scalable method that enhances learning efficiency and generalization across various applications, making it significant for advancements in AI and machine learning.
Key Takeaways
- The {}Belief-RL method improves credit assignment in long-horizon tasks.
- Utilizes intrinsic rewards based on an agent's beliefs to enhance learning.
- Demonstrates superior performance compared to traditional outcome-based rewards.
- Generalizes well to out-of-distribution applications, including customer service.
- Increases interaction efficiency as the number of test-time interactions scales.
Computer Science > Machine Learning arXiv:2602.12342 (cs) [Submitted on 12 Feb 2026] Title:Intrinsic Credit Assignment for Long Horizon Interaction Authors:Ilze Amanda Auzina, Joschka Strüber, Sergio Hernández-Gutiérrez, Shashwat Goel, Ameya Prabhu, Matthias Bethge View a PDF of the paper titled Intrinsic Credit Assignment for Long Horizon Interaction, by Ilze Amanda Auzina and Joschka Str\"uber and Sergio Hern\'andez-Guti\'errez and Shashwat Goel and Ameya Prabhu and Matthias Bethge View PDF Abstract:How can we train agents to navigate uncertainty over long horizons? In this work, we propose {\Delta}Belief-RL, which leverages a language model's own intrinsic beliefs to reward intermediate progress. Our method utilizes the change in the probability an agent assigns to the target solution for credit assignment. By training on synthetic interaction data, {\Delta}Belief-RL teaches information-seeking capabilities that consistently outperform purely outcome-based rewards for Reinforcement Learning, with improvements generalizing to out-of-distribution applications ranging from customer service to personalization. Notably, the performance continues to improve as we scale test-time interactions beyond the training horizon, with interaction-efficiency increasing even on Pass@k metrics. Overall, our work introduces a scalable training strategy for navigating uncertainty over a long-horizon, by enabling credit assignment to intermediate actions via intrinsic {\Delta}Belief rewards....
