Add configurable gate metric (hard / soft / mixed) for skill validation

The training gate currently always compares candidate vs. current/best
using *hard* exact-match accuracy. On environments with a small
held-out selection set (e.g. 3-6 items) or partial-credit scoring,
hard accuracy is too coarse: candidate skills that meaningfully
improve per-item soft scores get rejected because the discrete hard
count does not move.

Add three opt-in metrics so users can pick the one that matches their
scoring function:

- `gate_metric: hard`  — original behavior (default, fully backward
  compatible).
- `gate_metric: soft`  — gate on the soft / F1 / partial-credit score.
- `gate_metric: mixed` — `(1 - w) * hard + w * soft`, where `w` is
  set by `gate_mixed_weight` (default 0.5).

Changes
-------
- `skillopt/evaluation/gate.py`: extend `evaluate_gate` with
  `cand_soft`, `metric`, and `mixed_weight` keyword arguments; add a
  pure helper `select_gate_score(hard, soft, metric, mixed_weight)`.
  Defaults preserve the original `metric="hard"` behavior — existing
  callers that only pass `cand_hard` keep working unchanged.
- `skillopt/evaluation/__init__.py`: export the new helper / type.
- `skillopt/engine/trainer.py`: read `evaluation.gate_metric` and
  `evaluation.gate_mixed_weight` from the config (with safe defaults),
  pass both metrics into `evaluate_gate`, and project the baseline
  `current_score` / `best_score` into metric space so subsequent
  comparisons are consistent. Print the gate metric on the
  `[6/6 EVALUATE]` line so logs make the decision basis explicit. The
  selection cache still records both `(hard, soft)` so a metric change
  on resume is non-destructive.
- `configs/_base_/default.yaml`: document and ship the new keys with
  backward-compatible defaults (`hard`, `0.5`).

Backward compatibility
----------------------
- Default config does not change behavior: `gate_metric` defaults to
  `hard`, exactly matching the previous gate.
- `evaluate_gate(...)` keeps its existing positional signature; the
  new parameters are keyword-only with safe defaults.
- `step_record.json` gains optional `gate_metric` and
  `candidate_gate_score` fields; old records still load.

Tested
------
- Unit-tested all three metrics + boundary `mixed_weight` values
  (0.0 / 1.0) and rejection of unknown metric strings. All six cases
  pass.
- Verified `skillopt.engine.trainer` imports cleanly after the
  refactor.

configs/_base_/default.yaml

@@ -71,6 +71,12 @@ optimizer:
 
 evaluation:
   use_gate: true
+  # gate_metric: 'hard' (default, backward-compatible),
+  #              'soft' (use soft/F1 score),
+  #              'mixed' ((1 - w) * hard + w * soft).
+  # See skillopt/evaluation/gate.py for details.
+  gate_metric: hard
+  gate_mixed_weight: 0.5
   sel_env_num: 0
   test_env_num: 0
   eval_test: true