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Google research team proposes Sleep Framework, enabling large models to learn while in use without forgetting old knowledge
The Google research team has released a continual learning study called the "Sleep Framework," which divides model operation into two phases—awake and sleep—to address the problem of large models' rigid knowledge and difficulty in absorbing new information. The sleep phase writes short-term context into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and generates synthetic samples via "dreaming" for reinforcement learning. Experiments show that the framework can handle contexts of up to about 10 million tokens, that performance degradation is close to zero in multi-stage continual learning on unseen languages, and that it achieves higher accuracy on mathematical benchmarks such as AIME 24/25.
The Google research team has released a continual learning study called the "Sleep Framework," which divides model operation into two phases—awake and sleep—to address the problem of large models' rigid knowledge and difficulty in absorbing new information. The sleep phase writes short-term context into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and generates synthetic samples via "dreaming" for reinforcement learning. Experime
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LimX Dynamics Releases FluxVLA Engine V2 Technical Report, Adding Online Reinforcement Learning and Real-Robot Deployment Validation
LimX Dynamics has updated the second version of the technical report for its open-source engineering foundation FluxVLA Engine. Compared with the first version in September, it adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluation and real-robot deployment. The report shows that 14 policies were integrated into the same LIBERO closed-loop evaluation, and the inference frequency of 8 model-device combinations increased to 2.31–9.64 times; after online reinforcement learning, SmolVLA's success rates on LIBERO-10 and the ten tasks of RoboTwin Clean increased by 10.0 and 15.4 percentage points, respectively. The deployment pipeline has been validated on platforms including ALOHA, dual-arm Franka, Rebot, and LimX Oli.
LimX Dynamics has updated the second version of the technical report for its open-source engineering foundation FluxVLA Engine. Compared with the first version in September, it adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluation and real-robot deployment. The report shows that 14 policies were integrated into the same LIBERO closed-loop evaluation, and the inference frequency of 8 model-device combinations increased to 2.31–9.64 tim
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LimX Dynamics updates the FluxVLA Engine V2 technical report, adding online reinforcement learning and real-robot deployment evaluations
LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 percentage points, respectively; the inference frequency of 8 model-and-device combinations increased to 2.31–9.64 times. On the real-robot side, the deployment pipeline from model checkpoint to closed-loop execution was validated on ALOHA, the dual-arm Franka, Rebot, and the self-developed humanoid robot LimX Oli.
LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 perce
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Anthropic Research: How AI Is Changing Its Internal Hiring and Ways of Working
Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working six times as many hours as employees, and per-engineer code commits reached five times the level of six months earlier. Human attention has become the bottleneck on Agent output, employees are shifting toward meta-work of building monitoring and orchestration Agents, and hiring now places more value on autonomy and judgment.
Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working s
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