A CMU post describes three exploration regimes in reinforcement learning for language models and notes potential challenges in training on difficult tasks.
In a post published on January 31, 2026, the CMU blog describes three exploration regimes in reinforcement learning (RL) for language models: sharpening, chaining, and guided exploration. According to the post, standard RL uses the first two and can stagnate on difficult problems; mixing easy and difficult data can cause interference.
The challenges described may inform RL training design for difficult tasks, but the available summary does not detail methods or experimental results. To check the scope of the claims, consult the original post on the CMU blog and review its definitions and evidence. If you use AI to study the topic, avoid submitting personal data or internal information without applying your organization's policy.