Chain model training diverges after switching from lattice-free MMI to standard MMI in Kaldi nnet3

We run Kaldi nnet3 chain training on a mid-size corpus and are evaluating the two documented MMI variants: lattice-free MMI (the default in most chain recipes) and standard MMI with pre-generated lattices. After switching the training objective while keeping the same acoustic model topology, iVector input, and learning-rate schedule, the objective function oscillates and WER on the dev set regresses compared to the lattice-free baseline.

The relevant constraints: lattice-free MMI avoids the lattice-generation and lattice-alignment overhead, but the Kaldi documentation and recipe comments note it uses a different denominator graph construction (a phone-level decoding graph rather than utterance-specific lattices). Standard MMI depends on lattice quality from a prior alignment model, so a weak aligner could poison the denominator. We are unsure whether the divergence reflects a hyperparameter mismatch (e.g., the learning rate or l2-regularize settings tuned for LF-MMI) or a genuine lattice-quality problem.

• Should learning-rate and regularization settings be re-tuned when moving from lattice-free to standard MMI in nnet3, or are the chain recipe defaults expected to transfer?
• Is there a documented way to diagnose whether divergence comes from poor denominator lattices versus optimization instability?
• Does Kaldi expose any objective-level logging that distinguishes numerator versus denominator instability during chain training?