Topic
Trust Region Policy
2 episodes
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Overview: Reinforcement Learning from Human Feedback
We finally define reinforcement learning from human feedback the way we keep using it: as a loop where human preferences become a learned reward signal that steers a model after its initial training. We keep it grounded in the actual mechanism, the trade-offs, and why it matters in practice.
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CausalMix: Data Mixture as Causal Inference for Language Model Training
We unpacked CausalMix, the paper that treats data‑mixing as a causal inference problem. Cooper pulls the product angle—why it matters for shipping models, and Miles dives into the DML‑based plumbing and the trade‑offs. We talk about how it tackles shifting data pools, the CATE forest, and why the authors think it can generalize to larger models. A touch of banter and a light‑hearted sign‑off close the episode.