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Fine-tuning on planning-aware trajectories can enhance model performance across diverse CLI environments, mitigating the pitfalls of scaffold-specific training.
Coding agents struggle with non-functional improvements, scoring as low as 1.3 on structural changes compared to human developers' 1.5.
Fine-tuning a small language model with MindForge's innovative training environments boosts its performance to rival much larger models in software engineering tasks.
SpecFirst boosts program synthesis success rates by up to 21.3% by prioritizing behavioral specification before coding.
