Your framing is important, Linda, because simulation becomes a place to reason about behavior over time, not just to pretrain components.
MACHINE LEARNING
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Baichuan AI Achieves State-of-the-Art Results Across Multiple Benchmarks
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SOTA sweep:
HealthBench 65.1 / Hard 44.4 Hallucination 3.5% (lower than ChatGPT) ScanBench all-stations #1: 74.9 / 72.1 / 74.4 -
SPAR, Fact-Aware RL, and Rubric Evolution in AI Training
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Key takeaways:
SPAR: align RL credit to where decisions happen — optimize stage-wise, not via one noisy end reward. Fact-Aware RL: verify atomic claims with retrieval → make hallucination measurable & optimizable
Rubric Evolution: auto-mine & patch adversarial reward hacks. -
JLM Deployment Sharing Across Multiple Training Jobs for GPU Optimization
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2/5 The setup: JLMs are used only during the reward phase of Online RL, sitting idle the rest of each step. To avoid GPU waste, we shared JLM deployments across multiple training jobs. This improved utilization but exposed JLMs to unpredictable traffic bursts from multiple
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Scaling vLLM: Doubling Throughput and Halving Latency
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1/5 Go Big or Go OOM: The Art of Scaling vLLM .
We doubled throughput and cut latency in half-same GPUs, just better vLLM config then added smart autoscaling to handle traffic bursts. Here's what we learned optimizing LLM-as-a-Judge for GRPO training. -
Boston Dynamics Atlas demonstrates rapid progress in embodied AI
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Boston Dynamics just dropped a new Atlas video and it’s a reminder of how fast embodied AI is progressing. What stands out isn’t just agility or balance, but full-body control under real-world constraints. Every movement reflects years of work in perception, coordination and
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Google’s 2025 AI Research Breakthroughs Across Eight Key Areas
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Google's year in review: 8 areas with research breakthroughs in 2025 https://
buff.ly/oZu9r2M
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Google Reveals Energy Consumption Data for AI Prompts
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Google has released data on how much energy an AI prompt uses
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @demishassabis @Ronald_vanLoon @TamaraMcCleary @geoffreyhinton @goodfellow_ian @jeffdean @erikbryn -
Understanding Q, K, V Matrices in Transformer Architecture
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The Q, K, V Matrices In transformers https://
buff.ly/tNQFkov
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Modern Computer Vision with PyTorch — 2nd Edition Book
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2nd Edition, 746 pages, massive! Modern Computer Vision with #PyTorch #DeepLearning — from practical fundamentals to advanced applications and Generative AI: http://
amzn.to/3xAkB7X v/ @PacktDataML ——
#DataScience #MachineLearning #ML #GenAI #DataScientist
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