Data & training

The training side: datasets, synthetic data, pre- and post-training methods, compute and training cost.

Latest picks

121–124 of 124

Oct 1, 2024Tuesday

OpenAI NewsOct 1, 18:04

Introducing vision to the fine-tuning API

OpenAI launched GPT-4o vision fine-tuning on Oct 1, 2024, letting paid-tier developers train with images plus text, starting from as few as 100 images. The post cites Grab improving lane-count accuracy by 20% and speed-limit sign localization by 13%, while Automat raised RPA success from 16.60% to 61.67%. The notable shift is multimodal customization in the main API; the pricing section is truncated, so full price details are not disclosed.

Why it matters: OpenAI shipped a substantive API update: GPT-4o vision fine-tuning with a 100-image floor and named gains from Grab and Automat, so HKR-H/K/R all pass. Scope is strong for builders, but the blast radius is narrower than a flagship model launch, and pricing is incomplete in the ex

Take

OpenAI putting vision fine-tuning into the GPT-4o API makes the 100-image threshold the practical hook. Grab used only 100 examples and got +20% lane-count accuracy and +13% speed-limit sign localization. Automat moved an RPA agent from 16.60% to 61.67% success on screenshot-based UI targeting. That is a real workflow pattern: small proprietary visual data on top of a general multimodal model.

The catch is cost. The article says paid-tier developers can use it, but the captured pricing section is truncated, so training and inference prices are not given. For enterprise vision, a 7% F1 lift or 272% uplift only matters if labeling and calls stay cheap. Compared with tuning or hosting smaller vision models like Florence or PaliGemma, OpenAI wins on time-to-production; the bill decides the rest.

OpenAI NewsOct 1, 18:02

Model Distillation in the API

OpenAI launched an API distillation workflow on October 1, 2024, letting developers use outputs from GPT-4o and o1-preview to fine-tune cheaper models such as GPT-4o mini. The suite includes Stored Completions, Evals in beta, and fine-tuning; setting store:true auto-saves input-output pairs with no added latency, per the post. Pricing includes 2M free GPT-4o mini training tokens per day and 1M for GPT-4o through October 31; Evals are free up to 7 runs per week through year-end if shared with OpenAI.

Aug 20, 2024Tuesday

OpenAI NewsAug 20, 18:00

Fine-tuning now available for GPT-4o

OpenAI has opened GPT-4o fine-tuning to developers on all paid tiers, with 1M free training tokens per org per day through September 23. Training costs $25 per 1M tokens, and inference costs $3.75 per 1M input tokens and $15 per 1M output tokens on gpt-4o-2024-08-06. The signal for practitioners: partners reported 43.8% on SWE-bench Verified and 71.83% on BIRD-SQL with fine-tuned GPT-4o.

Why it matters: This is a substantive OpenAI developer release with concrete details: temporary free training quota, train/inference prices, base model version, and two benchmark datapoints. HKR-H/K/R all pass, but this is an API capability expansion, not a new frontier-model launch or platform-

Take

GPT-4o fine-tuning is a pricing move before it is a capability story. Training costs $25 per million tokens, with inference at $3.75 input and $15 output. That forces application teams to price their private data, output format constraints, and domain behavior against a higher serving bill.

The benchmark hooks are strong: Cosine reports 43.8% on SWE-bench Verified, and Distyl reports 71.83% execution accuracy on BIRD-SQL. But both are partner cases, not a general transfer curve. Fine-tuning shines when the target is narrow: patch formatting, SQL generation, intent routing, self-correction loops. The article does not give ablations for broad agent behavior. I’d treat the SOTA claims as proof that curated task data still matters, not evidence that every GPT-4o wrapper should fine-tune next week.

Jul 24, 2024Wednesday

OpenAI NewsJul 24, 17:00

Improving Model Safety Behavior with Rule-Based Rewards

OpenAI said on July 24, 2024 it uses Rule-Based Rewards in the RLHF pipeline to reduce repeated human feedback for safety alignment. The post defines three response types—hard refusal, soft refusal, and comply—and says the method has been part of OpenAI’s safety stack since GPT-4, including GPT-4o mini. The key point is maintainability when policies change; the post excerpt does not disclose quantitative gains.

Why it matters: HKR-H/K/R all pass: explicit rules inside RLHF is a strong hook, and the post adds three response modes plus paper/code. I keep it in the 78–84 band because the excerpt does not disclose effect sizes, baselines, or failure-case detail.