According to @DeepLearningAI, researchers introduced GAIN-RL, a method that fine-tunes language models by training on the most useful examples first using a simple internal signal from the model, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, on Qwen 2.5 and Llama 3.2, GAIN-RL matched baseline accuracy in 70 to 80 epochs instead of 200, roughly 2.5 times faster, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, this acceleration can cut compute costs and shorten iteration cycles for teams building math- and code-focused assistants, which is directly relevant for trading assessments of AI training efficiency and cost structures, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0.