[Podcast] JustRL: Is Simpler AI Better? скачать в хорошем качестве

[Podcast] JustRL: Is Simpler AI Better?

https://arxiv.org/pdf/2512.16649 JustRL: Scaling Reasoning Models via Simplified Reinforcement Learning The provided research paper introduces JustRL, a streamlined framework for training small language models to perform complex mathematical reasoning using reinforcement learning. Contrary to the current trend of utilizing intricate multi-stage pipelines and dynamic hyperparameters, this approach employs a minimal, single-stage recipe that remains stable over thousands of training steps. By maintaining fixed hyperparameters and avoiding common "tricks" like explicit length penalties, the researchers achieved state-of-the-art performance on 1.5B parameter models while using significantly less computational power than more complex methods. Evaluation across nine benchmarks demonstrates that JustRL-DeepSeek and JustRL-Nemotron outperform sophisticated models, proving that simplicity at scale can overcome the limitations of distillation. The authors argue that many existing training instabilities may actually be caused by unnecessary complexity rather than fundamental flaws in reinforcement learning. Ultimately, the study offers a validated baseline and open-source code to encourage the community to prioritize robust, foundational methods over elaborate technical interventions. #ai #research #reinforcementlearning