AutoSelection discovers data recipes from a 90K instruction pool that outperform full-data training and other selectors on reasoning tasks for SFT across multiple models.
W., and Dasigi, P
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Positive-negative prompt pairing with weighted GRPO improves RLVR sample efficiency, raising AIME 2025 Pass@8 from 16.8 to 22.2 on Qwen2.5-Math-7B while matching large-scale training.
One training example via RLVR boosts LLM math reasoning from 17.6% to 35.7% average across six benchmarks.
DRIFT applies on-policy influence functions with signed weighting and debiasing to attribute and refine SFT data, raising performance on 7B instruction and reasoning models over prior curation methods.
Extends influence functions with a second-order pairwise interaction term that improves group attribution accuracy over simple summation on multiple model-dataset pairs and instruction-tuning selection tasks.
HEAL mitigates entropy collapse in few-shot RLVR by selectively adding general-domain data and aligning trajectory-level entropy dynamics, matching full-shot performance with 32 target samples.
GIST recovers a task-specific low-dimensional subspace from validation gradients using SVD and scores training examples by their alignment within this coupled subspace for LoRA-based instruction tuning.
ADAPT is an online reweighting framework for LLM training that outperforms offline data selection and mixing methods in cross-benchmark generalization under equal compute.
Influcoder distills decoders' gradient influence rankings into an encoder for scalable influence-based data attribution.
citing papers explorer
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Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing
Positive-negative prompt pairing with weighted GRPO improves RLVR sample efficiency, raising AIME 2025 Pass@8 from 16.8 to 22.2 on Qwen2.5-Math-7B while matching large-scale training.
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DRIFT: Refining Instruction Data via On-Policy Data Attribution
DRIFT applies on-policy influence functions with signed weighting and debiasing to attribute and refine SFT data, raising performance on 7B instruction and reasoning models over prior curation methods.
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HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
HEAL mitigates entropy collapse in few-shot RLVR by selectively adding general-domain data and aligning trajectory-level entropy dynamics, matching full-shot performance with 32 target samples.
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GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
GIST recovers a task-specific low-dimensional subspace from validation gradients using SVD and scores training examples by their alignment within this coupled subspace for LoRA-based instruction tuning.
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Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
ADAPT is an online reweighting framework for LLM training that outperforms offline data selection and mixing methods in cross-benchmark generalization under equal compute.
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Influcoder: Distilling Decoders' Gradient Influence Rankings into an Encoder for Data Attribution
Influcoder distills decoders' gradient influence rankings into an encoder for scalable influence-based data attribution.