Unsupervised PRMs derived from LLM probabilities achieve up to 15% better error detection than LLM judges and match supervised PRMs in verification and RL tasks.
Decoupled Weight Decay Regularization
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2026 3verdicts
UNVERDICTED 3representative citing papers
Normal alignment is the rank-one Jacobian structure that lets classifiers minimize loss and maximize local robustness in sparse regimes; the paper proves its optimality and uses it to create GrokAlign and RFAMs.
DynamiCS dynamically scales semantic clusters per training epoch to reduce VLM pre-training compute while improving accuracy on long-tail concepts compared to static or flattening baselines.
citing papers explorer
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Unsupervised Process Reward Models
Unsupervised PRMs derived from LLM probabilities achieve up to 15% better error detection than LLM judges and match supervised PRMs in verification and RL tasks.
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The Geometric Structure of Models Learning Sparse Data
Normal alignment is the rank-one Jacobian structure that lets classifiers minimize loss and maximize local robustness in sparse regimes; the paper proves its optimality and uses it to create GrokAlign and RFAMs.
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Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training
DynamiCS dynamically scales semantic clusters per training epoch to reduce VLM pre-training compute while improving accuracy on long-tail concepts compared to static or flattening baselines.