Fix expected_rule in gradient and tangent_line benchmarks - #33
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…rk_gradient.json and benchmark_tangent_line.json had expected_ruleset to the literal operation name ('gradient', 'tangent_line'), which doesnot exist anywhere in the 13 real RULE: tokens in vocab.json. This does notaffect today's exact-match accuracy (run_eval.py's accuracy calc is purelyexpr-comparison based, expected_rule is currently unread there), but itwould silently produce a permanent 0% the moment any rule-level accuracymetric is implemented -- unrelated to model quality.problem_generator.py's generate_gradient_diff() and generate_tangent_line_diff()already assign rule_id=7 (partial_derivative) and rule_id=0 (power_rule)respectively (see inline comments at those return statements). This alignsexpected_rule with what the model is actually trained to predict.Verified: all 50/50 rows in each file updated; cross-checked againstvocab.json's actual rule_tokens list -- zero invalid labels remain.
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