Topic
Supervised Fine Tuning
8 episodes
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GitHub FareedKhan Dev/train LLM From scratch: A straightforward method for training your LLM, from downloading data to generating text.
A single-GPU end-to-end LLM training guide lands on GitHub—hand-written PyTorch, pretrain to chat in one repo, plus full RLHF. Vince is giddy; Ava wants to know which corner of the GPU shelf this actually runs on. Build Next shows the exact CLI to kick it off on a T4.
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Overview: Fine tuning on Execution Traces
We keep throwing around fine-tuning on execution traces, so we finally define it from the ground up. We’re talking about training a model on the steps, not just the answer, and why that changes what it learns.
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Overview: Train Test Split
We finally slow down on train-test split, because we keep bumping into it every time we talk about whether a model actually learned something. We use the sealed-final-exam picture to make the training set, validation set, test set, overfitting, leakage, and cross-validation click without assuming ML background.
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Overview: Supervised Fine Tuning
We finally slow down and make supervised fine-tuning click, because we keep leaning on S F T like everyone already has the whole shape of it. We build it from the apprentice-and-worked-examples picture into the actual training loop, the examples, and the trade-offs.
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VideoChat3:Fully Open Video MLLM for Efficient and Generalist Video Understanding
VideoChat3 is a fully open-source video multimodal LLM (4B parameters) that tackles three concrete problems: generalization across short/long/streaming video, computational efficiency for video token explosion, and reproducibility through complete open-sourcing. The core innovation is I3D-ViT (Inflated 3D Vision Transformer) plus adaptive frame resolution, which compresses spatiotemporal redundancy early in the pipeline instead of treating each frame as an independent image. Three curated datasets (2M academic + 116K long-form + 617K streaming = 3M samples total) and multi-stage curriculum learning enable the model to handle diverse video scenarios. Jessica sees a shippable foundation for real-world video apps; Cathy pushes on whether the efficiency gains hold under production load and whether the data pipeline's scale claim is reproducible.
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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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Improving Agents is a Data Mining Problem
Laura and Harper dig into Vivek Trivedy's claim that improving agents is fundamentally a data-mining problem, unpacking what that means for continual learning, harness engineering, and who should actually care.
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Qwen AgentWorld: Language World Models for General Agents
Justy and Cody dig into Qwen-AgentWorld, a new language world model that simulates seven agent environments. Cody breaks down the three-stage training pipeline (CPT, SFT, RL) and explains why a world model is the missing piece in agent development. Justy connects it to product reality: who ships this, what it actually unlocks, and whether it’s ready beyond the paper. They finish with cautious excitement and a quick Build Next check-in.