GeneralMath and ScienceCUATotalMMMUMathVistaScreenSpot-V21M150K450K1.6M44.037.448.21M150K850K2.0M44.137.360.01M450K450K1.9M45.336.048.31M450K850K2.3M43.438.963.11M150K150K1.3M44.236.929.81M150K250K1.4M45.437.437.7Table 2: Varying the ratios of math and CUA data. Increasing math data by 3x while keeping computer-use data constant improves both math and computer-use benchmarks.
All of this only works if accountability stays with the approving team regardless of who opened the PR. Who made the change and how they made it doesn’t matter. If someone changes something owned by your team, you review it, you approve it, you own the consequences. This requires crediting reviewers more than authors for dirt-cheap boilerplatey code, but that clarity will make the incoming non-engineer contributor model work. Putting PMs on-call would be punitive and ineffective since they’d still need an engineer to action any fix. The better path is investing in pre-checks that reduce the load on your reviewers, same as you would for any contributor who isn’t building deep context in your codebase.
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