Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Year: 2026
Type: conference-paper
Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards.However, their performance remains far from satisfactory due to the substantial annotation cost and issues such as model collapse or reward hacking.To address these issues, we introduce a new perspective inspired by cognitive learning theory and propose a novel approach called EasyRL.The core of EasyRL is to simulate the human cognitive acquisition curve by integrating reliable knowledge transfer from easy labeled data with a progressive divide-and-conquer strategy that tackles increasingly difficult unlabeled data.Specifically, we initialize a warm-up model using supervised RL with few-shot labeled data.This is followed by a divide-and-conquer pseudolabeling strategy on difficult unlabeled data, combining consistency-based selection for lowuncertainty cases and reflection-based resolution for medium-uncertainty cases.Finally, difficulty-progressive self-training with iterative pseudo-labeling and RL further strengthens the model's reasoning capability.EasyRL provides a unified self-evolving framework that facilitates data-efficient post-training of LLMs.Experimental results on mathematical and scientific benchmarks demonstrate that EasyRL, using only 10% of easy labeled data, consistently outperforms state-of-the-art baselines.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166813400