AWS is running the AWS Trainium Frontier competition to let teams co-design language-model training setups and optional custom kernels for AWS Trainium, to see what architectures perform best under different hardware constraints. Phase 1 uses a single Trainium2 chip with a 30-minute training budget scored by validation bits-per-byte (val_bpb). The competition is progressing from a single-chip, single-metric test to a four-hour, top-10-team Phase 2 that also adds a 50/50 composite including CORE inference performance.
Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs was published by Manning as a post-training textbook built from the author’s earlier documentation of RLHF methods. The book is 50% off until August 19 using code PBLambert, with online access plus a 12-hour course and shipping now from Manning/Amazon US and later in October from Amazon UK. Readers are given 25% RL-focused intuitions, systems guidance, and a fuller account of RLHF’s history and distillation/optimization pitfalls as a single reference.
Meta Superintelligence Labs released Muse Glimmer, while Mark Zuckerberg defended AI distillation and criticized labs that keep the strongest models closed. Muse Glimmer is a 30 billion-parameter model available on Hugging Face under the Apache 2.0 license. Meta’s product direction shifts toward more open releases, including a stated “soon” plan to resume open-source model publishing and signals that Muse Spark 1.2 will get open weights in the coming weeks.
Efficient Knowledge Distillation for LLMs proposes caching teacher top-K logits offline and using a fused, chunked KL-divergence loss to avoid building full vocabulary-by-sequence probability grids during training. The work reports cutting peak VRAM from about 250GB with dense KL to about 128GB with fused chunked KL, enabling long-context distillation on a single H200-class GPU. As a result, training cost drops enough for more large-scale experiments, and the authors claim near-lossless match to online distillation at 8K context while scaling to 32K–256K contexts with much lower memory.
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