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New RL methods tackle credit assignment for complex tasks · 2 sources tracked

By PulseAugur Editorial · [3 sources] ·

Two new research papers introduce novel approaches to enhance credit assignment in reinforcement learning (RL) for agents operating in complex, long-horizon tasks. The first paper, "Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning," proposes Explanatory World Models (EWM) which leverage language model explanations to attribute rewards to earlier, relevant actions, outperforming standard RL methods on benchmarks like Crafter and WordCraft. The second paper, "SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents," introduces SOAR, a framework that disentangles external and internal knowledge sources and uses source-consistent rewards to mitigate evidence fabrication in search agents, showing significant performance improvements and reduced fabrication on 3B and 7B backbones. AI

IMPACT These new RL frameworks could improve agent performance in tasks requiring long-term planning and reduce the tendency for agents to fabricate information.

RANK_REASON The cluster contains two academic papers published on arXiv detailing novel research methodologies in reinforcement learning.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New RL methods tackle credit assignment for complex tasks · 2 sources tracked

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Oriol Corcoll, Qiyao Wei, Ciar\'{a}n Mark Gilligan-Lee ·

    Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning

    arXiv:2610.13504v1 Announce Type: cross Abstract: Generative Recommenders increasingly use Reinforcement Learning (RL) to optimize outcomes that may appear hundreds or thousands of interactions after a decision. Standard RL propagates reward according to temporal proximity rather…

  2. arXiv cs.CL TIER_1 English(EN) · Shengze Huang, Zihao Tang, Jinluan Yang, Xin Yu, Zhenghao Lin, Xiao Liu, Zengxuan Wen, Yeyun Gong, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang, Fei Wu, Kun Kuang ·

    SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents

    arXiv:2610.13157v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents often suffers from evidence fabrication, a failure mode where agents bypass external information ($E_{ext}$) and fabricate an incorrect answer. We argue that this fabrication arises from…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ciarán Mark Gilligan-Lee ·

    Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning

    Generative Recommenders increasingly use Reinforcement Learning (RL) to optimize outcomes that may appear hundreds or thousands of interactions after a decision. Standard RL propagates reward according to temporal proximity rather than explicit relevance, attenuating the signal a…

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