Nathan Lambert @natolambert

One of the coolest at-scale RL resources made public yet! You love to see it. [引用 @_LuoFuli]: Nearly half a year of silence. We spent it studying one problem: how far RL can scale. MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-c…

• Open-source AI reading list posted on Hacker News2026-09-14