The RL system is implemented with an asynchronous GRPO architecture that decouples generation, reward computation, and policy updates, enabling efficient large-scale training while maintaining high GPU utilization. Trajectory staleness is controlled by limiting the age of sampled trajectories relative to policy updates, balancing throughput with training stability. The system omits KL-divergence regularization against a reference model, avoiding the optimization conflict between reward maximization and policy anchoring. Policy optimization instead uses a custom group-relative objective inspired by CISPO, which improves stability over standard clipped surrogate methods. Reward shaping further encourages structured reasoning, concise responses, and correct tool usage, producing a stable RL pipeline suitable for large-scale MoE training with consistent learning and no evidence of reward collapse.
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You type nix develop. The terminal fills with a single cryptic line: copying path, 47 of 312, 28.3 MiB, something something NAR. Five seconds. Ten. Is it evaluating? Downloading? Both? You change one line in your config and wait again. When it finally drops you into a shell, you switch to another branch and direnv hijacks your prompt for a rebuild you didn't ask for. You switch back, and Nix evaluates everything from scratch, even though nothing changed.