fix(spreadsheetbench)+optimizer: fix verify-feedback bloat, drop optimizer-side truncation, soft-disable gate

A. SpreadsheetBench verification-feedback bloat
   - rollout.py _auto_verify_output: use official _compare_cell_value (was
     repr() equality, which falsely flagged 5 vs 5.0 / None vs ""); collapse
     correct-and-empty cells into a count so large sparse answer ranges no
     longer flood feedback with MBs of None=None noise.
   - codegen_agent.py _build_eval_feedback: only list WRONG cells, collapse
     correct ones into a count.
   Scoring is unaffected (evaluate() is independent); this only fixes the
   target model's multi-turn solving feedback.

B. Remove optimizer-side truncation (bloat source now fixed)
   - reflect.py: drop _MAX_TRAJ_CHARS cap and all per-field clips.
   - update_modes.py / clip.py / lr_autonomous.py: describe_item /
     short_item_summary no longer truncate; raise ranking/lr token budget.
   - trainer.py _format_step_buffer: full task_ids / target.
   - slow_update.py: full comparison samples.

C. Soft-disable gate
   - config.py / trainer.py: use_gate=false no longer raises; validation still
     runs but candidates are force-accepted (new force_accept branch + log).

Misc: aggregate.py merge token budget 4096 -> 16384.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

skillopt/envs/spreadsheetbench/codegen_agent.py

@@ -54,8 +54,8 @@ def _build_eval_feedback(verify_report: str) -> str:
     output and whether each cell is correct or wrong.
     """
     import re
-    lines = ["Your code executed successfully but produced incorrect results.",
-             "The following cells have wrong values:"]
+    wrong_lines = []
+    n_correct = 0
     for raw_line in verify_report.splitlines():
         raw_line = raw_line.strip()
         if not raw_line:
@@ -68,9 +68,14 @@ def _build_eval_feedback(verify_report: str) -> str:
         if m:
             cell, got_val, mark = m.groups()
             if mark == "✗":
-                lines.append(f"  {cell}: your output = {got_val} (WRONG)")
+                wrong_lines.append(f"  {cell}: your output = {got_val} (WRONG)")
             else:
-                lines.append(f"  {cell}: correct ✓")
+                n_correct += 1
+    lines = ["Your code executed successfully but produced incorrect results.",
+             "The following cells have wrong values:"]
+    lines.extend(wrong_lines)
+    if n_correct:
+        lines.append(f"  ({n_correct} other cells are correct.)")
     lines.append(
         "\nPlease analyze the spreadsheet data more carefully and fix the code. "
         "Return a complete corrected Python script inside a ```python``` block."