N-Queens post-training experiment proposed to test whether models learn transferable backtracking
HanchungLee · x · 2026-10-13
waterloointern proposes a testable experiment: post-train a model in an N-Queens environment requiring it to find all valid placements, expecting rollouts to teach algorithmic backtracking, then evaluate in a held-out maze environment against the baseline. If performance matches or drops, the model didn't learn generalizable search.
Hanchung Lee responds that this is essentially meta learning — models can learn to apply backtracking via rlm/dynamic workflows/optimizations, or MCTS over larger search spaces; the hard part is problem specification and environment formulation, not search itself.
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