All sections
Latest
Everything published, newest first.
The log
Entries 1–20 of 167 · by original publication date
3 entries
If “the model will eat it all,” do we still need document processing pipelines?
That contrast was the most interesting part of the class. A small model struggled to decipher a short sentence that a Spanish speaker could. A stronger model handled a visually dense page that previously might have required a specialized extraction workflow.6 min readdocument-processingmodel-evaluation
SFT Improved This Model. Did DPO Add Anything?
Implement reply-only SFT and DPO on a small language model. Read the T4 measurements, derive the preference loss and distinguish a better margin from an established accuracy gain.14 min readPyTorch post-trainingsupervised fine-tuning
1 entry
Language Models Memorize More as They Grow, as Text Repeats, and as the Prompt Lengthens
Setting: a language model is trained on a huge pile of text, and sometimes it can reproduce parts of that text word for word.Quick thoughtprivacylanguage-models1 entry
One Number Can Fix an Overconfident Classifier: Temperature Scaling
Setting: a classifier outputs a probability for its answer. If it is calibrated, the answers it gives 90% confidence are right about 90% of the time.Quick thoughtcalibrationevaluation1 entry
Why Attention Divides by the Square Root of the Key Dimension
Setting: in a transformer, attention lets each token weigh every other token. For each token it computes a query vector q, compares it with a key vector k for…Quick thoughttransformersattention1 entry
Sample Many Reasoning Paths, Then Vote on the Answer
Setting: chain-of-thought prompting asks a language model to write its reasoning step by step before answering.Quick thoughtreasoningdecoding1 entry
For a Fixed Compute Budget, a Smaller Model Trained on More Tokens Can Win
Setting: training a large language model costs a fixed amount of computing, and you choose how to split it between a bigger model (more parameters) and more…Quick thoughtscaling-lawspretraining1 entry
LoRA: Train Two Thin Matrices Instead of One Giant One
Setting: fine-tuning adapts a pretrained model to a task by updating its weights. For a very large model, updating all of them means storing a full copy per…Quick thoughtfine-tuningtraining1 entry
Why Serving a Language Model Runs Out of Memory Before It Runs Out of Compute
Setting: a language model writes one token at a time. To avoid recomputing everything it has already read, it stores two vectors, a key and a value, for every…Quick thoughtinferenceserving1 entry
The DPO Loss Is One Number: How Much More You Raised the Winner Than the Loser
Setting: to make a language model prefer better answers, you collect pairs: a chosen answer and a rejected answer for the same prompt.Quick thoughtpost-trainingpreference-learning1 entry
A Code Benchmark Is Only as Strict as Its Tests
Setting: code-generation models are usually scored by running the programs they write against unit tests.Quick thoughtevaluationcode-generation1 entry
A Verifier That Checks Each Step Beats One That Checks Only the Final Answer
Setting: a language model can solve a math problem by writing its reasoning step by step, and you can raise its accuracy by sampling many solutions and…Quick thoughtreasoningverification1 entry
Forward KL Covers, Reverse KL Commits: Which Direction You Distill In
Setting: distillation trains a small student model to imitate a large teacher. The loss that compares them is usually KL divergence, a measure of how far one…Quick thoughttrainingdistillation1 entry
Optimize Against a Reward Model Too Hard and the True Reward Falls
Setting: to train a language model on what people prefer, you fit a reward model, a second model that predicts which of two answers a person would pick, and…Quick thoughtalignmentreward-models1 entry
With Only a Handful of Runs, Report an Interval and Use the Interquartile Mean
Setting: in reinforcement learning, a run is one training attempt of an algorithm from scratch, and results vary from run to run.Quick thoughtevaluationreinforcement-learning1 entry
Two Models Differ by a Point: Which Significance Test Do You Run?
Setting: you evaluate two models on the same test set, and one scores a point higher. Is that a real difference, or luck in which examples ended up in the…Quick thoughtevaluationstatistics3 entries
How to Build an RL Environment With a Verifier in Python
Build a small RL environment in Python: a task generator, a verifier and two policies. Then show a loose verifier paying a policy that does no work, and test the verifier with hostile inputs.12 min readRL environmentsevaluations
Make a Tool an Agent Can Operate: Ten Rules from a Retrofit
Agents lose time on tools built for people: prompts, one exit code for everything, prose output and polling. Build a small CLI that fixes each one, and check it the way an agent will.11 min readagent toolingcommand-line interfaces
Read the Conceptual Reasoning Fellow Posting Before You Apply
A posting tells you what a role needs, and it often tells you more about who the role is for than a job title does.Quick thoughtanthropicfellowshipsContinued on the next page.
To browse rather than follow: topics, all articles and all notes. To learn in order: courses and the book.
