Amazon Web Services released Strands Decider 2B, an open-source decision model inspired by TypeSafe’s Jev that returns calibrated choices instead of generating text. It was released as fully open-sourced software on the same week OpenAI announced a similar offering. The release expands the growing set of decision models and targets agent workflows that can use lower-latency, lower-cost “decider” steps with confidence scores.
Clef announced two open-source decision models, Clef and Clef-flash, hosted on Workers AI for fast classification and agentic workflows. The reinforcement learning platform lets developers fine-tune these decision models using their own data. This adds both prebuilt decision-model options and an RL fine-tuning path for custom workflows.
Olmo-core 3 was released as an upgraded open training framework for large mixture-of-experts (MoE) language models. In benchmarks, increasing the expert pool from 8 to 128 while keeping 4 experts selected per token held active parameters per token to about 3.2B and reduced training throughput by less than 5%. The update changes the training stack by switching from FSDP to a DDP-based approach that keeps experts on GPUs and adds routing, throughput, and MXFP8 optimizations to scale efficiently toward trillion-parameter MoEs.
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