Z3-Verified Graphs ship 5k baseline dataset

Strong idea, but the real test is whether the dataset teaches transferable reasoning or just a very specific graph-coloring procedure. The Z3 verification and explicit backtracking signals are the part worth paying attention to; the rest is mostly about how well the task mix avoids overfitting to one search pattern.

• –JSON traces are a sensible choice for SFT if the goal is algorithmic supervision, not narrative imitation, because they reduce ambiguity and token waste.
• –Explicit `[backtrack]` markers should help supervised learning, but they may also teach a brittle surface format unless paired with varied task types and corrupted examples.
• –The graph topology mix is useful for curriculum learning, yet generalization to scheduling or optimization will depend more on shared constraint structure than on graph shape alone.
• –Highest-degree-first is a pragmatic heuristic for graph coloring, but the dataset will be strongest if it includes enough diversity that models cannot memorize one search policy.
• –The 5k baseline is enough to prove the pipeline, not enough to prove “o1-level” reasoning; scaling to 100k only helps if harder negatives and broader CSP variants are added.