@bradenjhancock: In other words: Humans are teaching teacher models how to teach other models the way good human teachers teach other hu…
Summary
Humans are training teacher models to teach student models in a step-by-step manner, penalizing leaps, to improve model intelligence.
Similar Articles
Interpretable and pedagogical examples
Research showing that iterative training of student-teacher neural networks produces interpretable teaching strategies, with the teacher learning to select or generate pedagogical examples that humans can understand and learn from effectively.
@jeremyphoward: I feel that the trend towards training models to autonomously go off and try to do everything themselves is anti-human.…
Jeremy Howard argues against training AI models to autonomously do everything, advocating instead for LLMs that support human learning, creativity, and iterative experimentation.
@lateinteraction: Indeed. But the next breakthrough for a far more scalable RL paradigm than GRPO is already here: Train your self-teache…
Introduces Pedagogical RL, a new paradigm where models learn to be self-teachers by using privileged information to actively sample successful and easy-to-follow trajectories, achieving up to 40% relative gains over GRPO and on-policy distillation methods.
@blc_16: MIT just released a new RL method called Pedagogical RL. The main lesson -> correct reasoning traces can still be bad t…
MIT introduces Pedagogical RL, a method that trains a teacher to produce trajectories that are learnable for a student by penalizing surprising steps, improving RL training efficiency.
@OpenAI: Training models involves many technical and social processes, so prevention of CoT grading has to be built into the pro…
OpenAI is improving safeguards to prevent chain-of-thought grading issues in model training, including real-time detection, accidental grading prevention, and stress tests.