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machine learning Sep 18 OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher Post-Training Driving Policies Without Rendering Every Step End-to-end driving policies are pre-trained with behavior cloning on human demonstrations, mapping raw sensor data to actions or waypoint trajectories. This works in open... machine learning Sep 18 MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving Getting RL Policies Off the Simulator and Onto Real Dirt Reinforcement learning promises superhuman autonomous driving, but nearly every RL policy that works in simulation collapses when transferred to a real vehicle. The gap is e... machine learning Sep 18 Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation Why Video Diffusion Hits a Wall at Long Sequences Video diffusion transformers process spatiotemporal token sequences that grow with both spatial resolution and temporal duration. A 14.3-second, 768p video produces thousands of la... machine learning Sep 18 Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL The Default Training Recipe for Language Agents Has a Hidden Blind Spot When you fine-tune a language model on agent trajectories, the training loss is applied only to the tokens the agent wrote. The environment responses, termina... machine learning Sep 17 Can an AI Scientist Start with a Dataset Instead of a Goal? What Happens When an AI Scientist Works Backward From Data? Every automated research system starts the same way: someone picks a question. That step, choosing what to investigate, is usually the one humans do before handing the re... machine learning Sep 17 Probabilistic Linear Explanations When a machine learning model denies someone a loan or flags a medical scan, the most natural question is "why?" Formal explainability has spent years trying to answer that question with mathematical rigor. The dominant approach f... machine learning Sep 17 Double descent is the principle of least action The double descent phenomenon is one of the most counterintuitive findings in modern machine learning. Train a model with a small number of parameters and test error falls as you add more, following the classical bias-variance cur... machine learning Sep 16 Knowledge as Orbit: Finite Collections as Phases of an Exactly Periodic Latent Generator Machine learning stores knowledge extensionally. Every item in a collection gets its own code, its own latent vector, its own row in a codebook. For X objects, you need X entries. The question this paper asks is whether a finite c... machine learning Sep 15 Show HN: Chess Cheater Detector A web tool called ChessCheaterDetector is offering players and tournament organizers a way to quantify how engine-like a chess game looks from a statistical perspective. The site at chesscheaterdetector.com takes a PGN file, runs... machine learning Sep 14 A Letter from a Machine Learning Engineer An anonymous email claiming to be from a machine learning engineer at a frontier AI lab has been published online, and its contents paint a picture of an industry running on hype rather than fundamentals. The author, writing from...