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The two normalization branches of MixtureSampler disagreed on all-zero input: temperature == 1.0 crashed with ZeroDivisionError at the divide, temperature != 1.0 silently degraded to uniform sampling via the max(w, 1e-12) clamp. Both are reachable from the annealing phase path in scripts/train.py, where TrainingPhase.dataset_weights validates >= 0 (not > 0) to intentionally allow dropping individual datasets. A user who zeroes out every dataset in a phase transition would get a mid-run crash on one branch and silent training-quality drift on the other. Add a single _validate_weights() helper that rejects empty, negative, or all-zero weight lists with a clear ValueError, and call it from both __init__ and update_weights. The TrainingPhase schema- level validator stays permissive because it can't know the effective mixture at config-parse time; sampler-level is where the hard gate belongs.
Naeemkh
approved these changes
Apr 22, 2026
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Summary
_validate_weights(weights, context)inkempnerforge/data/sampler.py; call it from bothMixtureSampler.__init__andMixtureSampler.update_weights.ValueError. Previouslytemperature == 1.0crashed withZeroDivisionErrorat the normalization divide andtemperature != 1.0silently produced uniform sampling via themax(w, 1e-12)clamp.TrainingPhase.dataset_weightsschema validator stays>= 0so individual datasets can still be zeroed out; the sampler-level gate catches the all-zero case that the schema cannot see at config-parse time.Closes #53
Test plan
uv run pytest tests/unit/test_mixing.py::TestMixtureSamplerZeroWeights -v(8 tests).