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Value-Rectified Distillation is proposed, a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem and improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over...
VACE, Validation-Gated Alternating CoEvolution, which alternates agentic reinforcement learning with trajectory-driven harness refinement, which alternates agentic reinforcement learning with trajectory-driven harness refinement.
This paper introduces HarnessEvolve, a self-evolving framework that learns from reference trajectories to achieve reliable agent self-evolution, and overcomes credit assignment failure by generating reference trajectories and aligning failed executions against them to extract error signals.
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