REGISTERED TAG
Datasets
Prepare, separate and review data used for training or evaluation.
Jev foundations: typed decisions outside text generation
Use Jev/System One as a small typed decision component with candidates, state, and probabilities alongside ordinary code.
Jev implementation lab: connect candidate generation, abstention, and E2E evaluation
Connect semantic judgment to deterministic candidate generation and safety boundaries, then design end-to-end evaluation with controls.
Learn the basics of training, loss and evaluation
Treat inference and training as different computations, and design evaluation before training.
RAG: retrieve knowledge instead of packing it into weights
Separate retrieval from generation for questions that need changing facts and traceable sources.
Change behavior with SFT, LoRA and QLoRA
Organize additional training by its purpose and its implementation method.
Choose continued pretraining and preference optimization
Identify the goal and explain why a more demanding training method is needed.
Adapt to Japanese: vocabulary, notation and tasks
Evaluate Japanese language ability separately from domain expertise.
Handle medical terminology: sources, negation, units and human review
Distinguish terminology lookup and document processing prototypes from validated diagnostic use.
Read AI papers into implementation decisions: claims, conditions, reproduction, and failure
A textbook for turning papers into practical experiments by separating a claimed improvement from its problem, conditions, reproduction, trade-offs, and failures.
Read s1: what to measure when improving reasoning with little data
Translate the 2025 s1 paper into implementation decisions through test-time scaling, budget forcing, data selection, reproduction, and limits.
Read DAPO: do not view LLM reinforcement learning as an algorithm alone
Read the 2025 DAPO paper through reward, length bias, asynchronous system design, reproduction, and reward hacking.
Introduction to Physical AI: Connecting the history of VLA from perception to safe behavior
Instead of viewing Vision-Language-Action as the magic of a universal robot, understand it as a connection between observation, coordinates, strategy, and control.
LeRobot practice: Make dataset, imitation learning, evaluation, and Sim2Real into one quality loop
Starting with LeRobot's data format and unified interface, learn the points of contact that are likely to fail from collection to actual machine evaluation.
Starting Physical AI without a robot: Safe learning procedure and decision not to proceed to the actual machine
It clearly states that the hardware is not running, and touches on the core of Physical AI just through data observation, simulation, and evaluation design.
Reading VLA papers to implementable designs: Comparative reading exercise of π0, SmolVLA, and GR00T
The novelty of a paper is not confused with performance ranking, but is broken down into behavioral expression, data, body, evaluation, and unverified boundaries.
Physical AI workshop: from data splits to policy evaluation and Sim2Real decisions
Turn public or synthetic episodes into reproducible splits, baseline and adaptation evaluations, failure analysis, and a justified decision not to proceed to hardware.