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30:11EN
Sequoia Capital· Jul 30, 2025PodcastFrom 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.
6:53EN
Matthew Berman· Jul 24, 2025Qwen3-Coder: An Open-Weight Coding Model and Its Command-Line AgentMatthew Berman · Forward FutureMatthew Berman examines Qwen’s July 2025 coding model, its training and benchmark results, and tries its command-line tool on a Snake game.
51:06EN
20VC with Harry Stebbings· Jul 18, 2025Podcast · 2 speakersScott Wu on Cognition's Windsurf Deal and the Future of AI CodingGuest Scott Wu · Host Harry StebbingsThe Cognition CEO explains the fast-moving acquisition, the role of AI agents in engineering, and his predictions for software development.
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· Jul 17, 2025Podcast · 2 speakersEnterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other HalfHost 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.
38:10EN
Cognitive Revolution "How AI Changes Everything"· Jul 16, 2025PodcastPlus Five for Format, Plus Ten if It CompilesHost 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.
15:55ENMatthew Berman· May 28, 2025AI Shutdown Resistance and Blackmail in Controlled Safety TestsMatthew Berman · Forward FutureMatthew Berman examines Palisade’s shutdown experiment, Apollo’s simulated scheming evaluations and Anthropic’s Claude 4 system card.
57:31EN
Unsupervised Learning: With Jacob Effron· May 22, 2025PodcastThe New Ruler for Models Is How Long They Can WorkHost 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.
15:13ENMatthew Berman· May 10, 2025Absolute 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.
38:10ENNo Priors: AI, Machine Learning, Tech, & Startups· May 1, 2025Podcast · 2 speakerso3 should wait exactly as long as the answer takesHost 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.
30:46ENNo Priors: AI, Machine Learning, Tech, & Startups· Apr 24, 2025Podcast · 2 speakersDeep Research Turned Web Browsing into a Trainable ProblemHost 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.
1:14:11ENCognitive Revolution "How AI Changes Everything"· Apr 5, 2025PodcastThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIHost 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.
1:56:38ENCognitive Revolution "How AI Changes Everything"· Mar 8, 2025Podcasto3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligenceHost 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.
12:30ENMatthew Berman· Mar 7, 2025QwQ-32B: A Smaller Reasoning Model and Its TradeoffsMatthew Berman · Forward FutureMatthew Berman examines Qwen’s reinforcement-learning recipe, tests a coding task, and compares benchmark scores and hardware requirements with DeepSeek-R1.
32:46ENSequoia Capital· Feb 25, 2025PodcastDon'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.
17:20ENMatthew Berman· Feb 23, 2025Vibe 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.
44:34ENUnsupervised Learning: With Jacob Effron· Feb 19, 2025PodcastThe Cheaper Intelligence Gets, the More of It We'll BuyHost 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.
18:05ENMatthew Berman· Feb 16, 2025OpenAI’s Coding-Competition Study: o1-ioi, o3 and the Limits of the ResultsMatthew Berman, Sam Altman · Forward FutureMatthew Berman interprets OpenAI’s 2025 paper on reinforcement learning and programming contests. The results are strong on the studied benchmarks, but do not establish AGI or remove humans from model development.
6:50ENMatthew Berman· Feb 12, 2025DeepScaleR: A Small Reasoning Model Tested on Competition MathMatthew Berman · Forward FutureMatthew Berman reviews a 1.5B model’s reported benchmark results and runs a quantized version locally on one AIME problem.
21:10ENMatthew Berman· Feb 11, 2025Sam 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.
2:16:17ENCognitive Revolution "How AI Changes Everything"· Feb 5, 2025PodcastA 10B Model Trained Across Three Continents — and an H200 for $1.49 an HourHost 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.
10:01ENMatthew Berman· Feb 5, 2025DeepSeek 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.
2:49:35ZH → English
Zhang Xiaojun Podcast· Feb 4, 2025Podcast · 2 speakersPan Jiayi on R1, o1, and Kimi K1.5: How Reinforcement Learning Teaches Models to ReasonGuest 潘嘉怡 · Host 张小珺Pan Jiayi, a Berkeley PhD student, walks through reasoning-model reports from DeepSeek, OpenAI, and Kimi, unpacking long chains of thought, GRPO, reward design, and distillation. R1-Zero’s “aha moment” shows how reasoning strategies can emerge through reinforcement learning.
52:30ENSequoia Capital· Jan 28, 2025PodcastMove 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.
1:48:20ENCognitive Revolution "How AI Changes Everything"· Jan 25, 2025PodcastFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TableHost 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.Load moreShowing 24 of 106