A 400k+ GPU-hour study shows RL scaling in LLMs follows predictable sigmoidal trajectories, with most design choices affecting efficiency rather than the performance asymptote, enabling accurate large-scale predictions via the ScaleRL recipe.
arXiv preprint arXiv:2401.04757 , year=
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SuperValid synthesizes capability-aligned OOD validation data to produce a training-free loss metric that correlates with downstream benchmark performance across model architectures, scales, and data distributions.
DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.
Humanity's Last Exam is a new 2,500-question benchmark at the frontier of human knowledge where state-of-the-art LLMs show low accuracy.
Seed1.5-VL is a compact multimodal model that sets new records on dozens of vision-language benchmarks and outperforms prior systems on agent-style tasks.
citing papers explorer
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SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling
SuperValid synthesizes capability-aligned OOD validation data to produce a training-free loss metric that correlates with downstream benchmark performance across model architectures, scales, and data distributions.
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DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.