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arXiv AI By Ilia Mahrooghi, Aryo Lotfi, Emmanuel Abbe

Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning

Read the original on arXiv AI →

Goldilocks RL proposes an adaptive data‑selection strategy for reinforcement learning in language models, using a Selector network to predict reward variability and prioritize questions that are neither too easy nor too hard. By continuously adapting to the model’s evolving abilities, Goldilocks improves training efficiency on large‑scale reasoning datasets, achieving up to 78% fewer optimization steps compared to standard GRPO. The approach addresses the sample‑inefficiency problem of sparse rewards in reasoning tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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