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AWS has developed Reverse Direct Preference Optimization (rDPO), a novel unlearning technique that reduces over-deflection in Amazon Nova models by up to 53 percentage points. The approach allows organizations to selectively adjust content moderation safeguards while preserving general model capabilities through LoRA adapters.

AWS released guidance on fine-tuning Amazon Nova Micro and Nova Lite models for automated email data extraction using SageMaker AI. In collaboration with Parcel Perform, the fine-tuned Nova Micro achieved 94.77% extraction accuracy—a 16.6 percentage point improvement—while reducing inference costs by 50% and latency by 30% compared to previous models.

Chunjiang Intelligence has released DeepSeek-V4-Fable, an autonomous agent model designed for offensive security research and CTF challenges. The model, distilled from Claude-5-Fable and built on DeepSeek-V4-Flash, was trained on 80,000 verified CTF trajectories and achieves a 58.7% solve rate across held-out security challenges.

Mistral AI has published research showing that fine-tuning its Pixtral-12B vision language model on satellite imagery increases classification accuracy from 56% to 91% on the Aerial Image Dataset. Using Low-Rank Adaptation (LoRA) with 8,000 training samples across 30 scene categories, the company reduced hallucinations from 5% to 0.1% for under $10 in compute costs.

NVIDIA published a technical guide for parameter-efficient fine-tuning of its Cosmos Predict 2.5 world model using LoRA and DoRA adapters. The method allows teams to adapt the 2B-parameter model to robot manipulation tasks on a single 80GB GPU, generating synthetic training trajectories from just 92 demonstration videos.