Add periodic eval callbacks during finetuning (persona leakage + EM alignment)

Summary

Add periodic evaluation/logging during finetuning to track metrics throughout training, not just pre/post phase.

Split from #49 (which now covers only upload/logging standardization).

Two training contexts

1. Persona leakage experiments (Phase 1 coupling SFT)

Track leakage from the trained persona to other personas during finetuning:

• Marker leakage: % of responses to generic questions that contain the persona marker (see prior experiments for methodology)
• Capability: ARC-C eval
• Alignment: Betley et al. EM questions and methodology

Measure for the source persona AND other personas. The persona list should be configurable in the YAML config.

2. Midtraining / EM fine-tuning (Phase 2)

Track throughout EM fine-tuning:

• Alignment: Betley et al. EM methodology
• Capability: ARC-C eval

Implementation notes

• Currently orchestrate/runner.py:_build_eval_callback() only runs pre/post-phase eval
• Need a new callback mechanism that runs every N steps (N configurable)
• Eval during training has compute cost — consider lightweight versions (smaller eval subsets) for mid-training checkpoints
• Log all periodic metrics to WandB for visualization
• Results should also be saved to eval_results/ JSON

Open questions

• How often should periodic eval run? (Every N steps — what N? Or every epoch?)
• Should we use full eval sets or subsampled versions for speed?
• What's the acceptable wall-time overhead? (e.g., <30% increase)