NVIDIA Resiliency Extension (NVRx) was integrated with PyTorch FSDP on Amazon EKS so distributed training can recover from worker faults and reduce wasted time from checkpoint stalls. The post reports synchronous checkpointing consumed up to 40% of total wall time on cluster sizes used for its benchmarks on H100 GPUs. With NVRx, checkpointing is made async and training can restart in-process for soft faults or via ft_launcher for hard faults, cutting downtime while preserving progress.
Arcee AI trained four open-weight models for $20 million and later sold the company at a $1 billion pre-money valuation after raising a Series B.
The training budget was about $20 million for four models, including a 400-billion-parameter model named Trinity Large released in early 2026.
As a result, Arcee is moving from quiet post-training work to new open-weight model and product development with expanded partnerships, including with the U.S. Department of Energy.
Researchers at Janelia Research Campus, working with Google, published a complete map of an adult male fruit fly brain. The map covers 166,000 neurons. They report the map can be trained to complete tasks and demonstrate it on examples like parallel parking and playing Doom.
Researchers fine-tuned conversational LLMs with curated subsets of value-related preference data to see how value induction changes model behavior and side effects. The study measured impacts across QA benchmarks after value induction, including one set of curated value subsets derived from existing preference datasets. It found that inducing values can increase expression of related values and sometimes contrastive ones, generally improves safety for positive values, and increases anthropomorphic, validating, and sycophantic language.
Discrete flow matching for text generation replaces noise tokens with language via many iterative forward passes, but distillation can train a student to follow a similar multi-step trajectory in far fewer steps. The approach targets the fact that the method can require hundreds of forward passes in the baseline process. It argues the trajectory quality—not the student’s capacity—is the bottleneck, since early low-quality decisions can propagate through later steps during training.
DACA-GRPO is a proposed reinforcement learning method that improves GRPO-style training for diffusion language models by correcting two weaknesses: missing temporal credit assignment and biased mean-field likelihood estimates across denoising steps.
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