feat: held-out validation during training

[ZouzouWP]

Until now, training loss was the only signal available during a run — no way to tell a policy was starting to overfit without stopping and evaluating it separately afterward, by which point the ideal checkpoint was already steps behind.

What changed

• New "Validation" section in training's advanced config: a single toggle drives dataset.eval_split (fraction of episodes held out from training) and eval_steps (evaluation cadence) together, since lerobot requires both or neither.
• Backend parses step N: eval_loss=X lines (emitted by lerobot_train whenever eval_steps > 0) into a new eval_loss field, exposed on both the live training status and the persisted metrics history (so the curve survives a page reload).
• The monitoring chart plots eval_loss as a second, sparser line alongside the training loss, so overfitting becomes visible while training is still running instead of only in hindsight.

flowchart TB
    A["Held-out episodes\n(e.g. 10%)"] --> B["Periodic evaluation"]
    C["Training loop"] --> D["Training loss\n(every step)"]
    B --> E["Validation loss\n(every N steps)"]
    D --> F["Monitoring chart"]
    E --> F

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Testing

Implemented and code-reviewed against the log-parsing regex and the chart's dedupe/merge logic, but not yet run end-to-end with eval_steps > 0 against a real training job — needs a validation pass to confirm the parsed values match lerobot_train's actual output format before merge.