#chain-of-thought
4 articles
01
·FREE·PAPER·9 min read
Chain-of-Thought from Scratch — What Changes When You Make a Model Show Its Work
Why does adding "think step by step" make a model solve problems it otherwise fails? A ground-up tour of few-shot CoT, zero-shot CoT and self-consistency — the serialization argument for why it works, the conditions where it doesn't, and how it relates to today's reasoning models.
02
·Large Language Models·★ MEMBER·PAPER·13 min read
Test-Time Scaling — How Models Get Better by Thinking Longer
The same model scores higher when you let it think longer. This article builds the idea from scratch: chain-of-thought as purchased compute steps, self-consistency by majority vote, verifiers that pick the winner, and o1-style models that learned the thinking itself — and what it means for compute to shift from training to inference.
03
·★ MEMBER·PAPER·9 min read
Paper explained: DEFT-RLVR — show a driving VLM the future trajectory too early and it fabricates its reasoning
Let a teacher model peek at the ground-truth trajectory while writing chain-of-thought annotations for driving, and severe hallucinations double from 29% to 50% — the paper calls this trajectory anchoring bias. This article walks through AD-MCQ, which turns planning into a verifiable multiple-choice task, and DEFT-RLVR (arXiv:2608.01755), which reveals trajectories only after the model commits to a decision, using nothing but the paper itself as the source.
04
·Large Language Models·FREE·PAPER·7 min read
The Science of Prompt Engineering — What Is Proven and What Is Folklore
Does telling a model to think step by step actually work? Working only from the Chain-of-Thought paper (Wei et al., 2022): what the experiments genuinely established — an ability that emerges only above a scale threshold — and where the line falls between that and the incantations everyone repeats.