MemQ improves LLM agent performance by using eligibility traces over provenance DAGs to assign credit to dependent memories, achieving top success rates on six benchmarks with largest gains on complex multi-step tasks.
Proceedings of the 34th International Conference on Machine Learning , pages =
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SAVGO unifies representation learning, value estimation, and policy optimization by embedding state-action pairs such that cosine similarity reflects action-value similarity, enabling similarity-kernel-guided policy improvement.
citing papers explorer
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MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs
MemQ improves LLM agent performance by using eligibility traces over provenance DAGs to assign credit to dependent memories, achieving top success rates on six benchmarks with largest gains on complex multi-step tasks.
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SAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control
SAVGO unifies representation learning, value estimation, and policy optimization by embedding state-action pairs such that cosine similarity reflects action-value similarity, enabling similarity-kernel-guided policy improvement.