CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models
Paper: https://
arxiv.org/pdf/2503.22342
Code: https://
github.com/lzhxmu/CPPO
MACHINE LEARNING
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CPPO Accelerates Group Relative Policy Optimization Training
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CPPO Boosts GRPO Speed by 8x on Mathematical Reasoning
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GRPO just got a speed boost! Xiamen University introduced Completion Pruning Policy Optimization (CPPO), which significantly reduces the number of gradient calculations and updates.
How fast? On GSM8K, it's 8.32× faster than GRPO, and on MATH, the speedup is 3.51×. -
Building SQL Layer for Distributed Database Course
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GitHub – talent-plan/tinysql: A course to build the SQL layer of a distributed database. https://
buff.ly/oxXh9lO
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
LLM Reasoning: Scaling Inference Compute and Chain of Thought Optimization
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A great talk on "learning to reason with LLMs" by Naom Brown(
@polynoamial
). – Scale compute at inference time, not just train time. Let the model "think longer" using RL to improve reasoning. More like System 2 thinking, but for LLMs. – Optimize the "chain of thought" with -
VideoMind: Chain-of-LoRA Agent for Long Video Reasoning
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Mind: A Chain-of-LoRA Agent for Long Reasoning Liu et al.: https://
arxiv.org/abs/2503.13444 #ArtificialIntelligence #DeepLearning #MachineLearning -
AI, Robots, and Closing the Data Gap with Freakonomics
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So fun talking with @StevenDLevitt of @Freakonomics @MostlyAdmiring about AI, robots, randomness, art, humanoids, and how @AmbiRobotics is closing the 100,000 year data gap! https://
freakonomics.com/podcast/can-ro
bots-get-a-grip/
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Automating Artificial Life Search with Foundation Models
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Akarsh Kumar – Automating the Search for Artificial Life with Foundation Models https://
buff.ly/Gjq7Btb
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Modern AI: Mathematics, Compute, and Data at Scale
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𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗠𝗔𝗚𝗜𝗖! 𝗜𝘁’𝘀 𝗷𝘂𝘀𝘁 𝗰𝗲𝗻𝘁𝘂𝗿𝗶𝗲𝘀 𝗼𝗳 𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝘀𝗰𝗮𝗹𝗲𝗱 𝗯𝘆 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (1600𝘀–1900𝘀):
From Newton to backpropagation.
→ Without it? No training. -
Quantization Tutorial: Reduce AI Model Operational Costs
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Bonus: We give a full tutorial on Quantization, including how it works, the different techniques, and more. Bonus 2: We have a detailed Google Colab for you in there! Want to dive deeper? Read our blueprint to reduce AI model operational costs with quantization:
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Model Quantization Reduces AI Operational Costs Without Quality Loss
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Some insights from our recent post with @DamiBenveniste on his (amazing) newsletter: The AiEdge Newsletter! In this post, we shared how model quantization can dramatically cut AI model operational costs by reducing memory footprints **without sacrificing output quality** with