EvolveR enables LLM agents to self-evolve via a closed loop of distilling interaction trajectories into strategic principles offline and retrieving them to guide online decisions with policy reinforcement, yielding better results on multi-hop QA benchmarks.
Task-core memory management and consolidation for long-term continual learning
3 Pith papers cite this work. Polarity classification is still indexing.
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PsychAgent combines memory-augmented planning, trajectory-based skill evolution, and rejection fine-tuning to create a self-improving AI psychological counselor that outperforms general LLMs in multi-session evaluations.
LifeAlign uses focalized preference optimization and short-to-long memory consolidation via dimensionality reduction to let LLMs align with new preferences while retaining prior knowledge.
citing papers explorer
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EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
EvolveR enables LLM agents to self-evolve via a closed loop of distilling interaction trajectories into strategic principles offline and retrieving them to guide online decisions with policy reinforcement, yielding better results on multi-hop QA benchmarks.
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PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor
PsychAgent combines memory-augmented planning, trajectory-based skill evolution, and rejection fine-tuning to create a self-improving AI psychological counselor that outperforms general LLMs in multi-session evaluations.
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LifeAlign: Lifelong Alignment for Large Language Models with Memory-Augmented Focalized Preference Optimization
LifeAlign uses focalized preference optimization and short-to-long memory consolidation via dimensionality reduction to let LLMs align with new preferences while retaining prior knowledge.