SakanaAI/RLT-7B: Reinforcement-Learned Teacher Student Model

RLT-7B is a 7.6 billion parameter autoregressive language model developed by Sakana AI. This model is a "student" model, distilled from a 7B Reinforcement-Learned Teacher. The core innovation lies in the Reinforcement-Learned Teachers (RLT) pipeline, where the teacher model is explicitly trained to generate high-quality reasoning traces, which are then used to distill knowledge into the student model.

Key Characteristics

• RLT Pipeline: Utilizes a novel Reinforcement-Learned Teachers approach for distillation, focusing on reasoning trace quality.
• Reasoning Optimization: Trained with supervised fine-tuning using specific hyperparameters and reasoning tags from Li et al. 2025 to enhance reasoning capabilities.
• Context Length: Features a notable context length of 131,072 tokens.
• Research Prototype: Intended for research and development, not commercial deployment, as an experimental prototype.

Evaluation and Resources

Evaluation of RLT-7B was conducted using the SkyThought library. Further details on the RLT pipeline, training methodology, and results can be found in the associated paper and the Sakana AI RLT repository.