Sakana Ai presents teachers (RLT) learned to reinforcements: Effective dists reasoning in LLMs using learning to reinforce on small scale

Sakana AI introduces a new framework for models of reasoning language (LLM) by emphasizing efficiency and reusability: Teachers paired by strengthening (RLT). Traditional strengthening learning approaches (RL) in LLM are plagued by clear reward signals and prohibitively high calculation requests. On the other hand, RLTS redefines the teaching-student paradigm by Training of smaller models to act as optimized instructorsProducing explanations step by step instead of solving problems from zero. This design discrepancy allows important gains in the quality of distillation, profitability and transferability between the fields, without the need for largely part footprints.

Rethink the learning of strengthening for teaching, not resolution

Conventional RL configurations lead to models to solve the problems independently using sparse and accuracy rewards. These models are often reused for teaching smaller models, generating traces of reasoning for distillation. However, the inadequacy between the RL (resolution problems) objective and actual use (teaching) leads to ineffectiveness. RLTS addresses this directly by inviting models with the problem and its solutionForcing them only to generate detailed educational explanations. The reward signal is dense and aligned with students: it measures to what extent the student model includes the explanation and reproduces the solution.

Basic concept: dense and aligned awards on students

The RLT training objective is built around two key reward terms:

• Solution score (RSS): Quantifies the student's ability to reconstruct the correct solution given the explanation and the problem.
• Explanation score (RKL): Measure to what extent the teacher's explanation is logical consistent from the student's point of view.

These are combined in a dense reward signal which encourages explanations that are both instructive and understandable. Above all, this bypasses the bottleneck of the exploration of the traditional RL, allowing Smaller models to train effectively via RL.

Surprising efficiency of small teachers

Sakana AI demonstrates that a 7B RLT parameter Surpass much larger LLM (for example, 32B +models) on distillation tasks on several difficult data sets, including likes 2025, Math 500 and GPQA Diamond. On a 17 km question corpus:

• RLT-7B Surpass Deepseek R1, Bespoke-7b and even Post-Worry RL traces.
• RLT-32B Surporm all 32B baselines at all levels, although it is distilled with a smaller teacher.

The impact is not only the effectiveness of the parameters –RLTs obtain better generalization, fewer formatting errors and higher interpretability.

Cold strengthening learning with RLT

Another case of critical use is RL Cold StartingWhere an initial model is amortized with external data before formal RL training. The traces generated by RLTS serve as a cold start -up material more efficient than those of larger models formed by RL. In fact, even without post-treatment or external refinement (for example, via GPT-4.1), the explanations generated by RLT produce higher performance gains after an RL adjustment.

Generalization outside the field and zero transfer

RLTS also show Solid zero transfer capacities. When applied to a new field, such as the “countdown” based on arithmetic – the traces formed by RLT allow student models to even exceed the direct RL on the new field. This indicates that the competence “to explain a solution” is more easily generalized between the tasks than the competence of “zero resolution”, providing evidence Better reusability of RL models focused on teaching.

Training pipeline: efficient and scalable

The training process is meager in calculation:

• 250 RL steps (~ 1 era), size of the batch 256, group size 64.
• Trained using a configuration to a node with QWEN2.5-7B-ISTRUCT.
• The code and pre-trained control points are available: Github

Unlike traditional RL pipelines, RLTs do not require post -processing, formatting corrections or verification filters –Raw outputs are directly usable.

Strengths of evaluation

Tl; Dr (100 words)

Sakana Ai presents teachers (RLT) learned by strengthening, a light but powerful framework for teaching LLMS to reason. Unlike the traditional RL models that learn by solving the tasks from zero, the RLT receives both the question and its solution and are formed to generate explanations step by step. This configuration aligns the RL rewards with the learning results of the students, allowing the RLT of parameters 7B to surpass much more important LLM in the distillation and cold start scenarios. The RLTs are profitable and transferable between the fields and eliminate the need for expensive post-processing-offering an evolutionary plan to build LLM compatible with reasoning using modest and open-source calculation tools.

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