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.
Proceedings of the 37th International Conference on Machine Learning , articleno =
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DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
A decoupling strategy optimizes object slots for holistic class identity during training and composes them at inference to achieve better generalization to unseen concepts in continual few-shot settings.
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.
citing papers explorer
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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.
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DIVE: Embedding Compression via Self-Limiting Gradient Updates
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
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Unlocking Compositional Generalization in Continual Few-Shot Learning
A decoupling strategy optimizes object slots for holistic class identity during training and composes them at inference to achieve better generalization to unseen concepts in continual few-shot settings.
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Understanding the Prompt Sensitivity
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.