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/optimizer/clip.py

@@ -57,7 +57,7 @@ def rank_and_select(
     # Build the edit pool description for the optimizer
     edits_desc = []
     for i, edit in enumerate(edits):
-        edits_desc.append(f"[{i}] {describe_item(edit, update_mode, max_chars=500)}")
+        edits_desc.append(f"[{i}] {describe_item(edit, update_mode)}")
 
     user = (
         f"## Current Skill\n{skill_content}\n\n"
@@ -74,7 +74,7 @@ def rank_and_select(
     try:
         response, _ = chat_optimizer(
             system=load_prompt(prompt_name), user=user,
-            max_completion_tokens=2048, retries=3, stage="ranking",
+            max_completion_tokens=16384, retries=3, stage="ranking",
         )
         result = extract_json(response)
         if result and "selected_indices" in result: