Disrupting a coordinated model-distillation campaign
Learn how OpenAI disrupted a campaign to extract protected model reasoning and is strengthening defenses against adversarial distillation.
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Learn how OpenAI disrupted a campaign to extract protected model reasoning and is strengthening defenses against adversarial distillation.
Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice.
LoRA support recently landed in TRL's AsyncGRPOTrainer with PR #7017 , and ships with TRL v1.14. The asynchronous trainer can now train an adapter instead of the full model, and it syncs only the LoRA adapter to vLLM. This post covers a real-world project built on top of it, where training and inference no longer shar…
Our recent results show that Nemotron is a strong, adaptable foundation for building world-class specialist models. Starting from Nemotron 3, our teams used supervised fine-tuning (SFT), reinforcement learning (RL), and feedback-driven inference to create systems that reached gold-medal level at both IMO 2026 and IOI…
Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whet…
Our latest paper, Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs , asks a question that the field has mostly left open: once a model has already been through structural compression, not just quantization, how well does that recovery step actually work, and what is the right way to…
Finetuning multi-vector models involves several components: the model itself, datasets, loss functions, training arguments, evaluators, and the trainer class. I'll have a look at each of these components, accompanied by practical examples of how they can be used for finetuning strong multi-vector models.
Knowledge distillation , training a smaller student model to match the performance of a larger teacher, is a well-known technique in Machine Learning. With the recent wave of open-source Large Language Models, such as gpt-oss , Qwen , GLM , or Kimi , it has become a mainstream research topic again. Deploying these ver…