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The concept of in-context self-evolution is formalized and VALVE, a validated-gated framework for long-horizon skill optimization is introduced, which establishes finite convergence, provides theoretical guarantees for future-task gain and drawdown, and derive the validation and evaluation holdout sizes required for a...
This work introduces ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it, separating test construction from source-code repair.
On long-horizon complex search, TRACE substantially improves base-model tool-use ability using pure RL, without a cold-start supervised fine-tuning stage, an agentic mid-training stage, or training on live-web data.
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