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Verification of coding agent outputs is now the bottleneck, not generation, and targeted design can significantly enhance performance while curbing reward hacking.
An 80B model that runs like a 3B? Qwen3-Coder-Next shows you can get competitive coding agent performance with a fraction of the active parameters, thanks to smart training.
LLMs can solve competitive coding problems much more reliably by actively searching for the *right* test cases, rather than relying on random or pre-defined inputs.
