Introducing Reinforcement-Learned Teachers (RLTs): Transforming how we teach LLMs to reason with reinforcement learning (RL). Blog: Paper: Traditional RL focuses on “learning to solve” challenging problems with expensive LLMs and constitutes a key step in making student AI systems ultimately acquire reasoning capabilities via distillation and cold-starting. Enter our... RLTs—a new class of models prompted with not only a problem’s question but also its solution, and directly trained to generate clear, step-by-step “explanations” to teach their students. Remarkably, an RLT with only 7B parameters produces superior results when distilling and cold-starting students in competitive and graduate-level reasoning tasks than orders-of-magnitude larger LLMs. RLTs are as effective even when distilling 32B students, much larger than the teacher itself—unlocking a new standard for efficiency in developing reasoning language models with RL. Code:show more

Another banger from Sakana. You guys are dropping fantastic papers every week! I can't keep up.

Surprised it took this long for people to find this trick

Great work! Just to share — in our NeurIPS 2024 paper (arXiv’ed a year ago), we also proposed the Learning by Teaching idea and showed how it can enhance LLMs during both training and prompting.

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Great drop

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Thank you for sharing. Here’s a distilled summary of the Reinforcement-Learned Teachers (RLTs) concept and its significance: ⸻ 🧠 Reinforcement-Learned Teachers (RLTs) Overview 🔹 What Are RLTs? RLTs are a new class of LLM-based teacher models trained via reinforcement learning (RL) to produce high-quality, step-by-step explanations for reasoning tasks. Unlike standard RL-trained solvers, RLTs focus on teaching—not just solving. ⸻ 🔹 How RLTs Work •They’re prompted with both a problem and its solution. •They’re trained to generate interpretable reasoning paths rather than just answers. •The goal is to make them effective at distilling this reasoning into student models. ⸻ 🚀 Key Innovations & Results 1.Teaching Over Solving: RLTs don’t just learn to solve tasks—they learn how to explain them clearly for downstream distillation. 2.Efficiency Gains: A 7B parameter RLT can outperform much larger LLMs (like 70B) in teaching effectiveness, particularly in distilling reasoning ability into smaller student models. 3.Cold Start Generalization: RLTs can initialize student models with no prior task exposure—significantly improving cold-start training scenarios. 4.Teacher Smaller Than Student: A 7B RLT can successfully distill into a 32B student, challenging assumptions about size-based hierarchy in knowledge transfer. ⸻ 📊 Performance Impact •Superior results in competitive reasoning benchmarks •Enhanced training of student LLMs on step-by-step reasoning tasks •Opens up new paths for RL-driven education paradigms in AI development ⸻ 🧩 Implications •More interpretable AI: Teaching-based RL improves clarity over black-box solutioning. •Efficient scaling: Smaller models can teach larger ones—reducing compute needs. •Better alignment and control: RLTs allow for more structured reasoning supervision via RL. ⸻ 🔗 Resources •📜 Paper •🧑‍💻 Code •📝 Blog ⸻ Let me know if you’d like a diagrammatic summary, use-case extrapolation, or integration suggestion into your own symbolic framework.

Good update 👌

excellent work..Just had one doubt..Do we use the teacher feedback along with reward to fine tune the student or its just reward?