Depth Scaling In Self-Supervised Rl Is Real But Recipe-Dependent
The work is described as challenging the conventional wisdom that reinforcement learning is not scalable by demonstrating continued gains at extreme depth.
The reported gains depend on using a different self-supervised objective and are not presented as simply dropping larger networks into standard RL algorithms like PPO or SAC.
A proposed future direction is distilling or pruning very deep teacher policies into shallower student models to reduce inference cost while retaining performance.
Forecasting-Pipeline-And-Physical-Limits
Operational systems now achieve useful forecast skill out to roughly 10–15 days.
Consumer and ambient sensors (e.g., doorbell cameras, car sensors, phone sensors) and social media posts could serve as supplementary weather observations, but their data quality is uncertain.
DeepMind’s AI weather forecasting approach uses supervised learning to map an estimated current state to a future state and iteratively feeds predictions back as inputs to generate multi-step forecasts.
Alternative Portfolio Frameworks And Products
The forward-cap portfolio concept invests based on estimated future market capitalizations rather than today’s market-cap weights.
In macroeconomics, “investment” primarily refers to firm spending for future production rather than households buying securities.
A bond ladder concentrated in the 0–5 year maturity range can produce a more systematic and certain outcome than exposure to 30-year Treasuries.