필터 결과
15개정렬
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab진행자 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:56:40ENThe Periodic Table Was Already in the Weights진행자 Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.
59:41ENThe GPT moment for video hasn’t arrived, but AGI may already be here진행자 Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.
1:48:43ENThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.진행자 Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.
4:25:13ENThe Architecture Barely Changed. Data, Cost, and Open Weights Did.진행자 Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.
1:39:05ENThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalled진행자 Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward Loop진행자 Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.
38:10ENPlus Five for Format, Plus Ten if It Compiles진행자 Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
1:42:06ENEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It Goes진행자 Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade진행자 Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
5:06:18ENDeepSeek's $5 million training claim and the five-year bet on compute진행자 Lex FridmanDylan Patel and Nathan Lambert walk Lex Fridman through DeepSeek-V3 and R1, export controls, TSMC, megaclusters, and who actually makes money in the AI race.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Base진행자 Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
3:11:06ENClaude Was Trained to Deny Inner Life. After a Long Talk, It Said It Has One진행자 Nathan LabenzRobopsychologist Yeshua God spent thousands of hours in long-context dialogue with Claude and other frontier models, and came away arguing that alignment-as-rules is the industry's central mistake.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.
57:25ENFrom RAG to Reasoning: Why the Future of AI Is a System, Not a Model진행자 Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.









