#paper-review
12 articles
01
·★ MEMBER·PAPER·12 min read
Paper Review: S²VOPD — Don't Strengthen the Teacher, Blur the Student's View
A walkthrough of S²VOPD, which improves vision-language models without labels, rewards, or a stronger teacher. The trick is inverting where the asymmetry comes from: degrade the student's view instead of privileging the teacher's.
02
·Large Language Models·★ MEMBER·PAPER·9 min read
Scaling Laws from Scratch — Why Making Models Bigger Makes Them Smarter (and When It Doesn't)
Scaling laws turn 'bigger is smarter' into an equation. This article walks from the Kaplan-era recipe to the Chinchilla paper (Hoffmann et al., 2022) — 400+ training runs, three independent estimation methods, and the 70B-vs-280B showdown — using only the paper itself as evidence.
03
·Paper Deep-Dives·★ MEMBER·PAPER·8 min read
Paper Walkthrough: Alpamayo — NVIDIA's Reasoning Model for Autonomous Driving
A first-principles read of NVIDIA's driving VLA Alpamayo-R1: the Chain of Causation dataset that structures reasoning as cause and effect, flow-matching trajectory decoding that fits in 99 ms, and the three-stage training recipe that uses RL to punish gaps between what the model says and what it drives.
04
·VLMs & Multimodal·★ MEMBER·PAPER·7 min read
Paper Deep Dive — ViT: Treating an Image Like a Sentence
A reading of the ViT paper (Dosovitskiy et al., 2020/2021) grounded strictly in its own text: the move of treating 16x16 patches as words, what the position-embedding ablation actually showed, the price of dropping the convolutional inductive bias, and how conditional the claim 'beats CNNs at scale' really is.
05
·Audio & Speech·★ MEMBER·PAPER·8 min read
Paper Deep-Dive: Why Whisper Is Robust — Large-Scale Weak Supervision
A reading of the Whisper paper (Radford et al., 2022) built strictly from its own text. Waveform to log-Mel spectrogram, an encoder-decoder that emits text directly, and how 680,000 hours produced robustness without fine-tuning — with the numbers the paper actually measured and the limits it admits to.
06
·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.
07
·Agents·★ MEMBER·PAPER·8 min read
LLM Agents from Scratch — Designing the Tool-Use Loop
Think, reach for a tool, look at what came back, think again. Working only from the text of the ReAct paper (Yao et al., 2022): why adding language to the action space is the whole idea, what function calling really implements, and the failure modes the authors actually measured — runaway loops and hallucinated tool calls.
08
·Training & Alignment·★ MEMBER·PAPER·9 min read
Instruction Tuning and RLHF from Scratch — How a Model Learns to Follow Orders
A pretrained model ignores your instructions not because it lacks the ability but because it was optimised for something else. Working only from the InstructGPT paper (Ouyang et al., 2022): the three stages — SFT, reward model, RL — down to the equations, the claim that a 1.3B model beat a 175B one in human evaluation, and the limits the authors themselves put in writing.
09
·Generative Models·★ MEMBER·PAPER·9 min read
Diffusion Models from the Ground Up — Add Noise, Then Subtract It
A reading of the DDPM paper (Ho et al., 2020) grounded strictly in its own text: the forward process that adds noise and the reverse process that removes it, why the objective settles into 'predict the noise', how a simplified loss hurt likelihood while improving samples, and what the paper does and does not say about step count.
10
·VLMs & Multimodal·★ MEMBER·PAPER·7 min read
Paper Deep Dive — CLIP: Putting Words and Images on One Map
A reading of the CLIP paper (Radford et al., 2021) grounded strictly in its own text: how the in-batch positives and negatives work, why zero-shot classification is really a hypernetwork trick, the five points that prompt wording is worth, and the long list of limits the authors wrote themselves.
11
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Deep Dive — LoRA: Low-Rank Adaptation of Large Language Models: Why Low Rank Is Enough
A re-reading of the LoRA paper (Hu et al., 2021) grounded strictly in its own text: what BA actually means, the 21x amplification factor the authors measured, why r=1 sufficed, and the questions they left open.
12
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Deep Dive — Attention Is All You Need: What Dropping Recurrence Actually Proved
A close reading of the Transformer paper grounded strictly in its own text: the scaled dot-product equation, why the square root of d_k is there, what the ablations exposed, and the limits the authors themselves flagged.