arXiv Machine Learning
Marginal-Contribution Policy Gradients under Filtered Feedback for Multi-Agent LLMs
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Counterfactual Credit Policy Optimization for Multi-Agent Collaboration
arXiv:2603. 21563v4 Announce Type: replace Abstract: Collaborative multi-agent large language models (LLMs) can solve complex reasoning tasks by decomposing roles, but reinforcement learning for such systems is limited by credit assignment: shared terminal rewards obscure individual contributions and can encourage free-riding.
arXiv AI
Jun 3
Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System
arXiv:2602. 08335v2 Announce Type: replace Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving complex problems.
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Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
arXiv:2511. 10687v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation with agent- and message-level learning.
arXiv Machine Learning
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VIMPO: Value-Implicit Policy Optimization for LLMs
arXiv:2606. 20008v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become a central tool for improving the reasoning ability of large language models, but current methods face a trade-off between simplicity and credit assignment.
arXiv AI
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Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
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arXiv Machine Learning
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When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
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