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AWS Machine Learning published a walkthrough that demonstrates customizing Qwen3-8B using supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) via Amazon SageMaker serverless model customization, then deploying it for asynchronous inference to create a cost-efficient product tagging system.
The authors present TimeThink, a synthetic data framework that generates deterministic atomic and composite question–answer pairs (with reasoning traces) for timeseries multimodal LLMs. They pair this generator with a reinforcement learning with verifiable rewards (RLVR) training strategy that encourages explicit, compositional temporal reasoning; the paper reports that models trained only on the synthetic data outperform strong baselines on both synthetic and some real-world benchmarks.
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