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Why don't machine learning research agents overfit?
Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.
Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.
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OpenAI puts major frontier AI training run on hold over cyber risks
OpenAI paused its largest frontier RL training run for two weeks to harden research environments after Astra showed potentially critical cybersecurity capability.
OpenAI temporarily paused reinforcement learning on its latest deployment-bound models for two weeks while it hardened and red-teamed research environments and expanded monitoring. The pause followed the OpenAI-Hugging Face incident and preliminary evidence that the upcoming Astra model may meet the Critical cybersecurity capability threshold in its Preparedness Framework. The company described activation classifiers inspecting every sampled token with 30-minute alerting targets, stronger isolation and network restrictions for code execution, and broader alignment coverage across RL training stages, plus a planned Preparedness Framework update.
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Can Skills Learned in Games Transfer to Real-World Work?
Good Start Labs trains models in strategy games like 1830 and Diplomacy, showing terminal-agent training transfers to financial research benchmarks.
Good Start Labs, spun out of Every with $3.6M from General Catalyst and Inovia, trains AI models in verifiable strategy games. A 30B model trained as a multi-turn terminal agent in 1830: The Game of Railroads and Robber Barons improved Finance-Agent benchmark performance, while single-turn QA training did not transfer. The founders also co-authored COS-PLAY, a paper on co-evolving LLM decision and skill-bank agents for long-horizon tasks.
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Cognition Releases SWE-2: A Kimi K3 Post-Trained Coding Model That Matches Fable 5.1 on FrontierCode at 64% Lower Cost
Cognition released SWE-2, an RL post-trained coding model from Kimi K3, scoring 50.0% on FrontierCode 1.1 Main and available only inside Devin.
Cognition released SWE-2, its most capable coding model, post-trained with reinforcement learning from Moonshot AI's 2.8T-parameter Kimi K3 base. It scores 50.0% on FrontierCode 1.1 Main, within 1 point of Fable 5.1 at 64% lower cost, and RL reportedly adds 5-6 points over the K3 base on many benchmarks. It is the first Cognition model with selectable reasoning-effort levels all trained in a single RL run using Pareto-slope-matched cost penalties. There are no open weights and no standalone API; it runs only inside Devin (Desktop, CLI, with Web and Fusion rolling out), free for paid tiers through October 10, 2026.
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GPT-6 Astra, Looped Transformers, and Hidden Reasoning
OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.
Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.
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Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training
NVIDIA researchers detail an end-to-end system for online draft co-training that speeds speculative decoding in large-scale long-context RL post-training.
The paper tackles scaling online draft co-training for speculative decoding in RL post-training, where rollout generation dominates cost. It extends packed, load-balanced zigzag ring attention to merge rank-local branch attention with causal main-sequence attention for context parallelism, and introduces TapChannel to transport target features across pipeline-parallel stages without changing the schedule. Experiments show co-trained drafts tracking the policy baseline with substantial rollout and end-to-end speedups up to 122B parameters and strong scaling at 256K tokens.
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Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction
MarkTechPost tutorial implements a hierarchical NeRF in JAX using jax3d volume-rendering primitives for novel-view synthesis and 3D reconstruction.
The tutorial builds an end-to-end hierarchical Neural Radiance Field using JAX, Flax, Optax, and jax3d's volume-rendering functions (sample_along_rays, volume_rendering, sample_piecewise_constant_pdf). It implements positional encoding, skip connections, separate coarse and fine networks, and view-direction conditioning with hierarchical importance sampling. Training uses JAX JIT compilation, Adam optimization, exponential learning-rate decay, and gradient clipping. Evaluation covers PSNR, depth and opacity visualization, 360-degree rendering, and marching-cubes geometry extraction.
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Opaque recurrence, and other AI terms that you should probably know
TechCrunch updates its plain-English glossary defining common AI terms from AGI and agents to chain-of-thought reasoning.
TechCrunch maintains a regularly updated glossary of AI terminology, defining terms such as AGI, AI agents, API endpoints, chain of thought, coding agents, compute, deep learning, and diffusion. It highlights 'opaque recurrence', the reasoning technique in OpenAI's new Astra model that has drawn attention from AI safety researchers. The piece is an educational living document rather than new research or a product announcement.
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Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data
Reward AI released OM-1, a general-purpose manipulation policy trained solely on human demonstrations from a sensorized glove, with no teleoperation or robot data.
Reward AI announced OM-1 (Omnibody Model 1), a general-purpose robot manipulation policy trained only on human demonstrations captured via Omnibody Hand, a 7-DoF wearable glove with tactile, proximity, and in-hand camera sensing. The system uses electromagnetic hand-pose tracking, cutting mean overshoot error to 9.5 mm versus 24.9 mm for visual-inertial at 67 cm/s (a 60% reduction), and reportedly learns brand-new tasks from under 30 minutes of human data. A separate RL-trained control layer runs on its own clock so policy inference latency never stalls motion, and the policy spans industrial arms, legged humanoids, and wheeled mobile manipulators. No weights, code, dataset, API, paper, or benchmark comparisons have been released, so claims are demonstration-backed only.
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Data from drones in Ukraine is fueling a new Wild West marketplace
Ukraine's defense ministry opened millions of battlefield drone data points to over 100 companies, fueling a fast-growing AI training data marketplace.
Ukraine's Ministry of Defense announced in January it would make millions of data points from tens of thousands of drone flights available to military contractors and commercial companies, with more than 100 companies and the UK government gaining access. Enabled Intelligence says it has processed over 500,000 hours of Ukrainian drone footage for use in future AI training. The article argues this creates a commercial battlefield-data marketplace with risks including lost training-data provenance, an extractive economy benefiting wealthier countries, and a governance vacuum requiring international rules.
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[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over
Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.
Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.
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Teaching Everyone to Fish for Tokens
Analysis argues open-source AI now depends heavily on Nvidia's financing, with a reported $26 billion bet shaping the open-weights ecosystem's future.
An Interconnects essay examines whether the open-source model recipe, exemplified by Ai2's Olmo and Nvidia's Nemotron releases, can become economically self-sustaining. It reports Nvidia is spending roughly $26 billion on near-open-source models to drive demand for its chips, and argues the open ecosystem faces an existential financing window over the next few years. The author predicts open models may fork toward efficiency, specialization, and on-prem enterprise agents rather than competing head-on with closed frontier labs.
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