SPADE lets an LLM write its own training environments, edging toward self-improvement
Standard training pools freeze the goal distribution as a model scales. SPADE instead splits one LLM into an Environment Designer that writes executable, Gym-style training worlds in code and a Reasoning Agent that learns in them, with a regret signal that forces environments to track the edge of the learner's ability. At 30B parameters it beats the strongest fixed-environment baseline by +5.3 across eight held-out benchmarks and by +5.7 to +13.9 on tool-use tasks, and the margin grows with scale. It is a concrete step from curated data toward open-ended, recursive self-improvement.