
The Slop Harness: Why SOTA Models are Regressing in Tool Adherence
Advanced LLMs are becoming less reliable at following general tool schemas because they are being over-optimized for specific, forgiving internal harnesses.
The process of pretraining and training large language models from scratch, including data preparation, tokenization, optimization, compute requirements, and end-to-end training pipelines.

Advanced LLMs are becoming less reliable at following general tool schemas because they are being over-optimized for specific, forgiving internal harnesses.

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LLMs can significantly boost their code generation performance by fine-tuning on their own sampled outputs without any external guidance or verifiers.

An autonomous framework where AI agents independently iterate on and optimize LLM training code within fixed time budgets.

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