Retrieve-for-Train introduces a training-time RL method that compiles efficient, property-aligned query fan-outs so set-valued retrieval can run without test-time LLM “thinking” and latency overhead. It trains a 53.9M-parameter diffusion retriever that delivers a 12 to 20 speedup versus autoregressive approaches. The result is single-pass, sub-second-to-few-seconds inference that still optimizes set-level properties like diversity and groundedness instead of relying on expensive, token-heavy decomposition at query time.
Amazon SageMaker serverless model customization walkthrough customizes Qwen3-8B to generate product tags in a fixed nine-category schema using supervised fine-tuning (SFT) followed by reinforcement learning with verifiable rewards (RLVR).
Amazon SageMaker AI introduced instance preference lists for Training Jobs and Processing Jobs so a single job submission can automatically select the first available GPU instance type from an ordered list. The ordered list supports up to 5 instance types and launches on the first one with available capacity. Jobs no longer require manual retries or monitoring scripts and can automatically fall back to on-demand (including optional Flexible Training Plans priority), reducing time spent waiting to start.
Salesforce debuted Koa, a specialized AI model trained with Nvidia to reason over CRM data for use in its Agentforce platform. Koa was trained using a synthetic dataset built from nearly 30 years of internal CRM deployment experience and it cut CRM-task errors by allowing results with three times fewer errors than leading general-purpose models in Salesforce’s benchmarks. The model is being offered first through expanded pilots for select customers, with general availability planned for winter and Nemotron model access for Missionforce Operations rolling out in October within controlled customer environments.
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