Consistency Models: A New Paradigm for 1-Step Generation
Single-step generation without iterative sampling. OpenAI's innovative approach using self-consistency property.
Consistency Models: A New Paradigm for 1-Step Generation
Single-step generation without iterative sampling. OpenAI's innovative approach.
TL;DR
- Consistency Models: Map all points on the same trajectory to the same output
- Self-Consistency: $f(x_t, t) = f(x_{t'}, t')$ for all $t, t'$ on same trajectory
This part is for subscribers
A subscription unlocks every premium series and its Jupyter notebooks.
You need a free account to subscribe. Cancel anytime.
Related Posts
CFG-free Distillation: Fast Generation Without Guidance
Eliminating the 2x computational cost of CFG. Achieving same quality with single forward pass.
Same Model, Five Ways to Build the Input: Only Retrieval Moved It Significantly
On BANKING77 with GPT-5.6 Terra held fixed, reordering labels, grouping them and changing reasoning effort each moved one or two items in both directions against the baseline, with no significant paired difference. Attaching five retrieved training examples scored 146 of 154, fixing 19 items and breaking none (exact McNemar p about 3.8e-6).
Jev's Speed Claims: Benchmarking Label-Only Alternatives on BANKING77
Four ways to get only a label on BANKING77's 77-intent test set, compared on the same 154 messages. Pretrained MiniLM embeddings with a logistic regression reached 90.3% accuracy at a 6.8 ms median on CPU. Jev itself has not been measured yet.