Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
Measuring Mathematical Problem Solving With the
2 Pith papers cite this work. Polarity classification is still indexing.
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PRISM weights target examples by model preference to build an improved direction for influence-based data selection in LLM fine-tuning.
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PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning
PRISM weights target examples by model preference to build an improved direction for influence-based data selection in LLM fine-tuning.