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Don't Train the Model, Evolve the Harness

A new approach called "harness optimization" proposes evolving the training harness or data pipeline rather than retraining the model itself, aiming to improve performance more efficiently by focusing on the surrounding infrastructure.

Background

- "Harness optimization" is a new approach to improving AI models by tweaking the surrounding system (prompts, decoding settings, tool-use structures) rather than retraining the model itself. - The term "harness" refers to the orchestration layer that wraps a language model — how inputs are formatted, how outputs are parsed, and what tools or memory are available. Optimizing this can yield large performance gains with zero compute cost. - This idea matters because training large models is extremely expensive (millions of dollars, massive energy use). If you can get better results by engineering the harness better, it shifts focus from scaling up models to smarter system design. - The Hugging Face Space linked offers a benchmark or leaderboard for comparing harness optimization strategies, making the concept tangible for practitioners.

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