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learning with yacine show

yacine mahdid
Self-Distillation
Paradigma
Flywheel
Auto Research
Math Academy
Reinforcement Learning
Directed Acyclic Graph
Scientific Publication

a show about untangling the complexity of deep learning and the brain by interviewing smart researchers and builders in the field of intelligence! ideal for scholars, industry practitioners or toddlers. www.yacinemahdid.com

PublishesMonthlyEpisodes3Founded2 months ago
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ScienceTechnology

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Latest Episodes

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery.now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments is... more

I had an awesome time interviewing @IdanShenfeld and @jonashubotter from MIT and ETH Zurich about self-distillation.

this very promising post-training paradigm where the model acts as its own teacher by conditioning on environment feedback or demons... more

hey folks first time posting this sort of interview as a podcast (shout out the three anon on twitter that requested it).

now that I think of it the format does make sense since me and the interviewee are mostly chatting and exchanging ideas!

will ... more

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Training Data
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Recent Guests

Francesco Papone
Co-founder of Paradigma
Paradigma
Episode: Why DAGs Are Taking Over Auto-Research (w/ the Founders of Paradigma)
Idan Schentfeld
PhD student at MIT focusing on reinforcement learning algorithms for LLMs and robotics
Massachusetts Institute of Technology
Episode: Why Self-Distillation Is Taking Over LLM Post-Training (w/ the Researchers Behind It)
Jonas Huberter
PhD student at ETH Zurich researching test-time training and continual learning
ETH Zurich
Episode: Why Self-Distillation Is Taking Over LLM Post-Training (w/ the Researchers Behind It)

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Talking Points

Recent interactions between the hosts and their guests.

Why Self-Distillation Is Taking Over LLM Post-Training (w/ the Researchers Behind It)
Q: What are the practical implications for continual learning or production systems?
Self-distillation supports learning from diverse data streams, including live user interactions, with reduced forgetting and faster adaptation, enabling continual improvement in deployed models and potential online learning setups like OpenClaw RL.
Why Self-Distillation Is Taking Over LLM Post-Training (w/ the Researchers Behind It)
Q: What data sources can self-distillation learn from beyond standard rewards?
It can learn from expert demonstrations, feedback signals, rich environment signals, and even raw real user conversations, all integrated through the teacher's context to produce improved student behavior without reliance on scalar rewards.
Why Self-Distillation Is Taking Over LLM Post-Training (w/ the Researchers Behind It)
Q: What is the central idea of self-distillation and how does it compare to traditional RL or SFT?
Self-distillation uses the same model as both teacher and student, with the teacher receiving additional context. The learning signal comes from the KL divergence between the teacher's and student's output distributions, providing dense, token-level feedback that leads to faster convergence and less forgetting compared to RL and SFT.

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learning with yacine show launched 2 months ago and published 3 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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3. Jonas Huberter

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