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The results show that by-language token efficiency is a metric that should be considered when benchmarking and developing multilingual agents, and, for the tokenmaxxer, a guide to the most expensive language to work in.
It is shown that models fine-tuned on AgentPack can outperform models trained on prior human-only commit corpora, highlighting the potential of using public data from software engineering agents to train future code-editing models.
Agnostics introduces Agnostics, a language-agnostic post-training pipeline that eliminates per-language engineering, and releases the language-agnostic training datasets, making RL post-training in any programming language as simple as editing a short YAML file.
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