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Research

GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR

This paper introduces GIST, a two-level data selection method for offline reinforcement learning with verifiable rewards (RLVR). It derives a factorization of the KL divergence between the current policy and the Gibbs-optimal target, identifying which responses and tokens are most informative. GIST selects correct responses on-policy and incorrect responses near the policy's mode, and supervises only the tokens where the mismatch lies. Experiments show accuracy gains across GSM8K, MATH-500, and MBPP for three models, with 66.6% fewer supervised tokens in a controlled comparison.

arxiv1d agoScore 78

Reinforcement LearningData SelectionToken Efficiency

Research

OT-CAL uses optimal transport to adaptively select training samples and cut backprop cost

Researchers introduce OT-CAL, a data selection method that adjusts which samples receive backward passes as training progresses. Each sample is described by five informativeness features such as difficulty, learnability and uncertainty, and optimal-transport matching assigns selection weights against a curriculum vector that is updated from the selected samples, removing the need for a manual schedule. On three standard visual learning benchmarks, selecting 50% of each mini-batch improved results by 1.3-2.1 points over AdaSelection while training 1.18-1.24 times faster than full-data training.

arxiv4d agoScore 68

optimal transportdata selectiontraining efficiency