SPADE: RL Self-Play in Adaptive Synthetic Environments

SPADE is a reinforcement learning framework that enables a single large language model to achieve open-ended self-improvement by playing two distinct roles. One part of the model acts as an Environment Designer, writing executable Python code to create complex, multi-turn training tasks, while the other part acts as a Reasoning Agent that learns to solve them. To ensure these tasks are challenging yet possible, the system uses a hint-based regret reward, which encourages the designer to create environments that the agent can only solve when given a privileged tip. This dynamic creates a co-evolving curriculum where the training difficulty automatically scales as the model's capabilities grow. Research findings indicate that SPADE significantly outperforms static training methods across various benchmarks, including math, coding, and tool-use tasks. By making the creation of training data a learnable component, the framework moves toward autonomous AI development that does not rely on limited human-curated data.

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