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30:11EN
Sequoia Capital· 2025년 7월 30일팟캐스트From 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.모델과 학습Reasoning & test-time computeBenchmarks+3
51:06EN
20VC with Harry Stebbings· 2025년 7월 18일팟캐스트 · 2명Scott Wu on Cognition's Windsurf Deal and the Future of AI Coding게스트 Scott Wu · 진행자 Harry StebbingsThe Cognition CEO explains the fast-moving acquisition, the role of AI agents in engineering, and his predictions for software development.코딩 에이전트Software engineeringAcquisition Strategy+1
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 7월 17일팟캐스트 · 2명Enterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other Half진행자 Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· 2025년 7월 16일팟캐스트Plus 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.모델과 학습파인튜닝Post-training+3
57:31EN
Unsupervised Learning: With Jacob Effron· 2025년 5월 22일팟캐스트The New Ruler for Models Is How Long They Can Work진행자 Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
슬라이드 7장15:13EN
Matthew Berman· 2025년 5월 10일Absolute Zero: Training a Reasoner on Self-Generated Coding TasksMatthew Berman · Forward FutureMatthew Berman examines a 2025 paper in which a pretrained language model proposes and solves code tasks using verifiable feedback.Reinforcement LearningSynthetic dataReasoning & test-time compute+2
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 5월 1일팟캐스트 · 2명o3 should wait exactly as long as the answer takes진행자 Sarah Guo, Elad GilOpenAI researchers Eric Mitchell and Brandon McKinzie tell No Priors why o3's leap is a chain of tools during reasoning — and why you should send the same prompt a hundred times.모델과 학습Reasoning & test-time computeReinforcement Learning+3
30:46EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 4월 24일팟캐스트 · 2명Deep Research Turned Web Browsing into a Trainable Problem진행자 Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.에이전트에이전트 엔지니어링Reinforcement Learning+3
1:14:11EN
Cognitive Revolution "How AI Changes Everything"· 2025년 4월 5일팟캐스트The Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.모델과 학습Reasoning & test-time computeReinforcement Learning+3
1:56:38EN
Cognitive Revolution "How AI Changes Everything"· 2025년 3월 8일팟캐스트o3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligence진행자 Nathan LabenzOn Consistently Candid, the Cognitive Revolution host argues that math and coding will be superhuman by 2026, and that a reasoning core plus specialist “intuitive physics” may already be enough.모델과 학습Reinforcement LearningDistributed training & inference+3
32:46EN
Sequoia Capital· 2025년 2월 25일팟캐스트Don't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.에이전트에이전트 엔지니어링Reinforcement Learning+3
슬라이드 2장17:20EN
Matthew Berman· 2025년 2월 23일Vibe Coding a Login Page and an AI Tetris PlayerMatthew Berman · Forward FutureMatthew Berman experiments with AI-written code, iterates through failures and trains a basic game-playing agent.Vibe Coding코딩 에이전트Reinforcement Learning+1
44:34EN
Unsupervised Learning: With Jacob Effron· 2025년 2월 19일팟캐스트The Cheaper Intelligence Gets, the More of It We'll Buy진행자 Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.에이전트에이전트 엔지니어링Reinforcement Learning+3
슬라이드 3장21:10EN
Matthew Berman· 2025년 2월 11일Sam Altman’s 2025 AI Coding Forecast and Three ObservationsMatthew Berman, Sam Altman · Forward FutureMatthew Berman examines predictions about reasoning models, software agents and AI economics, distinguishing competitive-programming results from broader work.코딩 에이전트Scaling LawsReinforcement Learning+2
2:16:17EN
Cognitive Revolution "How AI Changes Everything"· 2025년 2월 5일팟캐스트A 10B Model Trained Across Three Continents — and an H200 for $1.49 an Hour진행자 Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.인프라와 배포Distributed training & inferenceAI cost & performance+3
슬라이드 5장10:01EN
Matthew Berman· 2025년 2월 5일DeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.Reinforcement LearningGPU programming & kernelsAI research+2
2:49:35ZH
Zhang Xiaojun Podcast· 2025년 2월 4일팟캐스트 · 2명潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理게스트 潘嘉怡 · 진행자 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告,拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。Reinforcement Learning모델과 학습AI research+2
52:30EN
Sequoia Capital· 2025년 1월 28일팟캐스트Move 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.에이전트Reinforcement Learning에이전트 엔지니어링+3
1:48:20EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 25일팟캐스트From 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table진행자 Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.모델과 학습Reinforcement LearningReasoning & test-time compute+3
슬라이드 2장30:29EN
Matthew Berman· 2025년 1월 3일How Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.Reinforcement Learning모델과 학습AI research+1
1:27:33EN
Cognitive Revolution "How AI Changes Everything"· 2024년 12월 3일팟캐스트A 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a Day진행자 Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.에이전트에이전트 엔지니어링Computer use+3
39:22EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 11월 14일팟캐스트 · 2명A Fields Medalist Stuck for Two Hours. AlphaProof Climbed for Three Days.진행자 Sarah Guo, Elad GilThe DeepMind team behind AlphaProof tells Sarah Guo and Elad Gil how an AlphaZero-style agent, a Lean verifier, and test-time reinforcement learning reached IMO silver — and why the missing skill is still theory building.에이전트Reinforcement LearningReasoning & test-time compute+1
45:22EN
Sequoia Capital· 2024년 10월 2일팟캐스트AlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.모델과 학습Reasoning & test-time computeReinforcement Learning+3
39:45EN
Sequoia Capital· 2024년 9월 24일팟캐스트Harmonic’s Bet: Teach AI Math Directly, and the Riemann Hypothesis Falls by 2029Robinhood’s Vlad Tenev and Harmonic co-founder Tudor Achim tell Sequoia why math is reasoning, why Lean is the verifier, and why they refuse to wait for mathematical skill to emerge from scale.모델과 학습Synthetic dataReinforcement Learning+3
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