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OpenAI vs. Anthropic, a mysterious 300B model, and the definitive AI model tier list (drinking game). What could go wrong? 0:00 Intro 3:30 OpenAI vs. Anthropic 14:44 Anthropic’s delayed models 20:53 Kimi K3 and open weights 30:08 The Alpha stealth model 48:26 Model tier list begins 1:09:53 GPT-5.6 Luna...

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Nerd Snipe

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117,810 Aufrufe • vor 29 Tagen •via X (Twitter)

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6 Kommentare

Profilbild von ShadowAguy
ShadowAguyvor 29 Tagen

tier lists are just astrology for people who think they understand weights

Profilbild von Pranav Bobde
Pranav Bobdevor 29 Tagen

Omg is long haired theo coming back!?

Profilbild von Panta
Pantavor 28 Tagen

Tier list peeve: you put too little weight on how convenient is to access a model. Eg. to compare mid-tier deals Grok: - comes with X account - comes with Cursor - flat usage plan vs Muse: - API pricing - Can't pre-pay - Can't put a spend limit, only notice

Profilbild von Skuffd
Skuffdvor 29 Tagen

Just what I need first thing in the morning

Profilbild von Imade
Imadevor 29 Tagen

Excited to see that harness tier list in next episode !

Profilbild von Adam Adeel
Adam Adeelvor 29 Tagen

20:53 Kimi K3 and open weights Guys cost isn't right or deepswe updated the cost

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How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models
1:16:04

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models

Matt Turck

451,229 Aufrufe • vor 11 Monaten

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts
1:28:10

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts

Matt Turck

37,531 Aufrufe • vor 10 Monaten

Reasoning from scratch round 3: This time, I cover generating a verifier for... a) ...evaluation (base model versus any future model improvement) b) ...the reinforcement learning with verifiable rewards (RLVR) training later on 00:00 Introduction 01:21 Four approaches to LLM evaluation 07:20 Verifiers and reinforcement learning with verifiable rewards 10:52 Notebook setup and dependencies 13:43 Section 3.1 Building a math verifier 18:57 Section 3.2 Loading a pre-trained model to generate text 24:34 Generating and displaying model answers 29:23 Section 3.3 Implementing a wrapper for easier text generation 34:00 Section 3.4 Extracting the final answer box 37:29 Handling answers without boxes 43:17 Section 3.5 Normalizing the extracted answer 46:56 Section 3.6 Verifying mathematical equivalence 53:32 Implementing the equality check 57:48 Section 3.7 Grading answers 59:20 Building and testing the answer grader 1:03:18 Section 3.8 Loading the evaluation dataset (MATH-500) 1:07:51 Section 3.9 Evaluating the model 1:08:34 Prompt templates for evaluation 1:10:47 Prompt sensitivity and memorization 1:13:55 A minimal evaluation example 1:15:32 Building the evaluation loop 1:20:27 Comparing CPU, MPS, and CUDA results 1:21:54 Reproducibility and floating-point math 1:23:37 Base model vs. reasoning model 1:25:30 Summary and next steps
1:26:46

Reasoning from scratch round 3: This time, I cover generating a verifier for... a) ...evaluation (base model versus any future model improvement) b) ...the reinforcement learning with verifiable rewards (RLVR) training later on 00:00 Introduction 01:21 Four approaches to LLM evaluation 07:20 Verifiers and reinforcement learning with verifiable rewards 10:52 Notebook setup and dependencies 13:43 Section 3.1 Building a math verifier 18:57 Section 3.2 Loading a pre-trained model to generate text 24:34 Generating and displaying model answers 29:23 Section 3.3 Implementing a wrapper for easier text generation 34:00 Section 3.4 Extracting the final answer box 37:29 Handling answers without boxes 43:17 Section 3.5 Normalizing the extracted answer 46:56 Section 3.6 Verifying mathematical equivalence 53:32 Implementing the equality check 57:48 Section 3.7 Grading answers 59:20 Building and testing the answer grader 1:03:18 Section 3.8 Loading the evaluation dataset (MATH-500) 1:07:51 Section 3.9 Evaluating the model 1:08:34 Prompt templates for evaluation 1:10:47 Prompt sensitivity and memorization 1:13:55 A minimal evaluation example 1:15:32 Building the evaluation loop 1:20:27 Comparing CPU, MPS, and CUDA results 1:21:54 Reproducibility and floating-point math 1:23:37 Base model vs. reasoning model 1:25:30 Summary and next steps

Sebastian Raschka

41,079 Aufrufe • vor 13 Tagen