docs: scope PR #25 gate_metric as opt-in example, not default

Move the soft/mixed gate-metric configuration introduced in PR #25 out of
the base default config and into a standalone example config so that
default SkillOpt runs (and paper reproduction) remain bit-for-bit on the
original hard gate.

- configs/_base_/default.yaml: drop gate_metric / gate_mixed_weight keys.
  The trainer's cfg.get("gate_metric", "hard") fallback preserves the
  original behavior unchanged.
- configs/examples/soft_gate.yaml: new standalone reference config with
  a header explaining when to consider it (small selection split with
  continuous rewards) and when not to (paper reproduction, large or
  binary-reward settings).
- README.md: add a short "Community-contributed configs" section that
  clearly flags this as user-contributed and non-default.

README.md

@@ -208,6 +208,23 @@ Re-running the same command auto-resumes from the last completed step.
 
 ---
 
+## Community-contributed configs
+
+These are **not** default SkillOpt settings — they are reference configs
+contributed by users for specific scenarios. The paper-reported numbers
+were obtained with the default settings, not these.
+
+- **`configs/examples/soft_gate.yaml`** *(PR #25, contributed by
+  [@lvbaocheng](https://github.com/lvbaocheng))* — switches the
+  validation gate from exact-match (`hard`) to soft / partial-credit
+  (`soft` or `mixed`). Useful when the held-out **selection split is
+  small** (e.g. ≤ ~10 items) and the **reward is continuous**, where the
+  discrete hard gate often rejects every candidate and training stalls.
+  See the comment at the top of the file for details and when not to use
+  it.
+
+---
+
 ## WebUI
 
 Launch the monitoring dashboard (optional):

configs/_base_/default.yaml

@@ -71,12 +71,6 @@ 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

configs/examples/soft_gate.yaml

@@ -0,0 +1,47 @@
+# ─────────────────────────────────────────────────────────────────────────────
+# Example: soft / mixed validation-gate metric (community-contributed, PR #25)
+# ─────────────────────────────────────────────────────────────────────────────
+#
+# This is NOT a default SkillOpt setting and was NOT used to produce the
+# numbers reported in the paper. It is provided as a reference for users
+# who encounter a specific scenario where the default `hard` gate is too
+# coarse to drive training.
+#
+# When to consider this:
+#   - You are running on a custom environment.
+#   - Your held-out *selection* split has very few items (e.g. ≤ ~10).
+#   - Your reward function is continuous / partial-credit (e.g. F1, BLEU,
+#     soft match) rather than purely binary 0/1.
+#
+# Symptom this addresses:
+#   With a small selection split + continuous rewards, candidate skills
+#   often improve per-item soft scores (e.g. 0.06 → 0.26 on one item) but
+#   never flip the discrete hard outcome. The default `hard` gate then
+#   rejects every candidate and training stalls. Switching the gate to
+#   `soft` or `mixed` lets these partial improvements be accepted.
+#
+# When NOT to use this:
+#   - When reproducing the paper. The paper-reported numbers were obtained
+#     under the default `hard` gate.
+#   - When your selection split is large (dozens+ items) and / or your
+#     reward is already binary — `hard` is the more conservative choice
+#     and matches the design described in the paper.
+#
+# To use: inherit your env config from this file, e.g.
+#   _base_: ../examples/soft_gate.yaml
+# or copy the `evaluation:` block below into your config.
+# ─────────────────────────────────────────────────────────────────────────────
+
+_base_: ../_base_/default.yaml
+
+evaluation:
+  # Three options:
+  #   'hard'  — default; exact-match accuracy. Use this to reproduce the paper.
+  #   'soft'  — per-item soft / partial-credit score (recommended for the
+  #             small-split + continuous-reward scenario described above).
+  #   'mixed' — weighted average: (1 - w) * hard + w * soft, with `w` set by
+  #             `gate_mixed_weight` below.
+  gate_metric: soft
+
+  # Only used when gate_metric == 'mixed'. Ignored otherwise.
+  gate_mixed_weight: 0.5