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9 diapositives22:05EN
Matthew Berman· 9 juin 2025Apple’s “Illusion of Thinking”: Puzzle Limits and a Code-Assisted TestMatthew Berman, Ilya Sutskever · Forward FutureMatthew Berman reviews Apple’s reasoning-model experiments and tests a different way to solve Tower of Hanoi using generated code.Reasoning & test-time computeBenchmarksÉvaluations+1
6 diapositives18:05EN
Matthew Berman· 16 févr. 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.Reasoning & test-time computeReinforcement LearningBenchmarks+2
5 diapositives15:33EN
Matthew Berman· 13 févr. 2025Latent Reasoning and Recurrent Depth: Scaling Compute Before the Next TokenMatthew Berman, Yann Lecun · Forward FutureMatthew Berman examines a 3.5-billion-parameter research model that repeats internal computations, and asks how it relates to language, planning and chain of thought.Reasoning & test-time computeModèles et entraînementAI research+2
8 diapositives21:21EN
Matthew Berman· 12 sept. 2024OpenAI o1’s 2024 Launch: Reasoning Benchmarks and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews o1-preview and o1-mini, OpenAI’s reported evaluations, and his own early coding experiments.OpenAIReasoning & test-time computeBenchmarks+2
5 diapositives22:17EN
Matthew Berman· 21 mars 2024Q* Rumors, Energy-Based Models, and Research on ReasoningMatthew Berman, Yann Lecun · Forward FutureMatthew Berman examines an anonymous, unverified account of Q* and compares its ideas with published research on teaching language models to reason.Reasoning & test-time computeModèles et entraînementAI research+2