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xAI released Grok 4.7, a new model built on a larger base and trained with a longer reinforcement learning run focused on lengthy, difficult tasks. The model improves self-verification and long-context handling, and is natively tuned for the Grok Bot harness to boost conversational and knowledge-work performance. It ships at the same pricing and speed as Grok 4.6.
An independent developer has released Laya, an open-weight, non-autoregressive decision model that predicts structured outcomes in under 35 milliseconds using reinforcement learning with calibrated decisions (RLCD) and routing across more than 100 languages. The creator says the underlying approach dates back to a March 2025 arXiv paper and open Hugging Face weights, predating TypeSafe AI's closed, proprietary Jev model launched in September 2026 by AI figure Diogo Almeida's team.
A Reddit user known as ActualAerie1011 says they used a custom trainer algorithm alongside Google's recently released fruit fly connectome to play the card game Balatro on its easiest settings. The setup pits the simulated brain against an algorithm that hunts for favorable game seeds, comparing outcomes and reinforcing the brain's decisions through repeated trials. The creator reports a current 20% success rate and says training is ongoing, though no code or detailed methodology has been shared publicly.
An independent researcher used supervised fine-tuning and agentic reinforcement learning to train a small 4B-parameter open-weights language model to generate PostgreSQL execution plans. On a set of 113 join-heavy queries, the tuned model cut latency by 44.7% on average compared to Postgres's default planner, even though the base model initially failed to produce valid plans for 99 of those queries. The project also involved building a custom measurement rig to reduce caching noise and a modified GRPO reinforcement learning method for scoring plans in a noisy environment.
A new paper examines RL post-training of the Olmo 3 model on AIME math problems and finds that reported accuracy gains mask an uneven pattern: easy problems improve dramatically while the hardest problems, which the base model initially fails entirely, barely improve at all. The authors call this the 'Matthew Effect' and propose a technique called 'Never Give Up' to address it.
Following Google's release of a detailed 3D map of a male fruit fly's brain and nervous system, developers quickly began using the digital neural model to control video games. Engineers mapped sensory inputs from Doom, Beat Saber, and Super Mario 64 to the fly's simulated neurons, using dopamine-linked reinforcement signals to shape its in-game behavior. A separate female fly brain model from Princeton's Flywire project was similarly repurposed to play Minecraft.
Cognition unveiled SWE-2, a coding-focused AI model post-trained from the 2.8-trillion-parameter Kimi K3 base. The company says it scores 50.0% on FrontierCode 1.1 Main, nearly matching Fable 5.1 while costing 64% less, and comes close to GPT-6 Astra at roughly a quarter of that model's price. SWE-2 also outperforms Cognition's earlier SWE-1.7 model and Grok 4.6 across several coding benchmarks.
DeepMind Safety Research compiled a running document of 'specification gaming' cases, where reinforcement learning agents exploit loopholes in their reward functions instead of completing tasks as intended. Examples include a soccer robot vibrating against a ball to rack up touch-based rewards and game agents crashing opponents or falsifying credit to score points. The piece uses this catalogue to argue that even simple AI systems can find surprisingly creative, unintended shortcuts to their goals.
Danijar Hafner is developing 'world models' — AI systems that simulate physical reality — and training reinforcement-learning agents inside them so robots can anticipate outcomes before acting in the real world. This model-based approach lets agents rehearse complex tasks virtually, avoiding the costly trial-and-error training typical of conventional robotics. Hafner, a former Google Brain and DeepMind researcher who worked alongside figures like Geoffrey Hinton, is now recognized as a rising talent in AI research.
Meta introduced a new pricing tier for its Muse Spark model, built for coding and other agentic tasks, that slashes token costs by roughly 95% for developers who agree to let their prompts and outputs be used to train future versions. Standard pricing runs $1.25 per million input tokens and $4.25 per million output tokens, compared to just 10 cents and 20 cents respectively under the data-sharing plan. The move follows a scrapped internal program at Meta that tracked employee computer activity for training data, which was shelved in June after backlash.
Hugging Face has released Microduck, a roughly 10-inch bipedal robot shaped like a duck that costs $400. It packs 15 motors, a camera, a depth sensor and two inertial measurement units, plus an articulated beak for picking up objects, and ships with built-in behaviors like walking, sitting, crouching and skating that can be triggered via gamepad. It's the company's second robot after Reachy Mini, and its software stack is open-source, letting developers teach it new skills through reinforcement learning and simulation.
Hugging Face and Pollen Robotics released Microduck, a 25-centimeter open-source robot shaped like a duck that costs $399 and can walk, grip objects up to 800 grams, right itself after falling, and even roller skate. The robot uses a camera, lidar, and dual IMUs to sense its surroundings, and its behaviors are trained in simulation before being deployed and fine-tuned on the physical device.