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Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge

In multi-turn reinforcement learning (RL), your custom reward function decides what the model actually learns. A subtly wrong reward can quietly teach the wrong thing while every training curve looks healthy. Designing a reward that holds up over multi-turn, agentic tasks is one of the hardest parts of customizing Amazon Nova models. For multi-turn training, …

AI agents struggle to perform original scientific research

Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.

Using BigQuery Graphs with measures for trusted agentic workloads

When enterprises transition from using simple chat assistants to autonomous, agentic workloads, they quickly run into a hard truth: Agents are prone to inaccurate insights when working with directly raw tables.  BigQuery Graph helps organizations move beyond flat, static tables to represent enterprises exactly how they exist in the physical world: as interconnected business entities …

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Part 2: Amazon Bedrock cost attribution with Amazon Athena and CUDOS

Part 1 introduced granular cost attribution for Amazon Bedrock. This feature automatically traces every inference request back to the IAM principal that made the call. It showed how the new line_item_iam_principal column can give you per-user and per-application visibility. With optional cost allocation tags, you can also aggregate spend by team, project, or tenant using …