The maximum reward gain under KL-regularized LM alignment is a Jeffreys divergence term, estimable as covariance from base samples, with best-of-N approaching the theoretical limit.
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3 Pith papers cite this work. Polarity classification is still indexing.
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VLA-ATTC equips VLA models with adaptive test-time compute via an uncertainty clutch and relative action critic, cutting failure rates by over 50% on LIBERO-LONG.
A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.
citing papers explorer
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Theoretical Limits of Language Model Alignment
The maximum reward gain under KL-regularized LM alignment is a Jeffreys divergence term, estimable as covariance from base samples, with best-of-N approaching the theoretical limit.
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VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model
VLA-ATTC equips VLA models with adaptive test-time compute via an uncertainty clutch and relative action critic, cutting failure rates by over 50% on LIBERO-LONG.
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Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data
A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.