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/rollout.py

@@ -26,7 +26,9 @@ from concurrent.futures import (
 import openpyxl
 
 from skillopt.envs.spreadsheetbench.react_agent import run_react
-from skillopt.envs.spreadsheetbench.evaluator import evaluate, _generate_cell_names
+from skillopt.envs.spreadsheetbench.evaluator import (
+    evaluate, _generate_cell_names, _compare_cell_value,
+)
 from skillopt.envs.spreadsheetbench.executor import run_generated_code
 
 
@@ -129,11 +131,30 @@ def _auto_verify_output(
                 lines.append(f"  Sheet '{sheet_name}' NOT FOUND in output.")
                 continue
 
+            n_correct_skipped = 0
             for cn in cell_names:
                 gv = ws_gold[cn].value if ws_gold else "N/A"
                 pv = ws_pred[cn].value
-                match = "✓" if repr(gv) == repr(pv) else "✗"
+                # Use the official cell comparator so this report's ✓/✗ agrees
+                # with the real scorer (evaluate). repr() equality would wrongly
+                # flag e.g. 5 vs 5.0 or None vs "" as mismatches and mislead the
+                # model into "fixing" cells that already pass scoring.
+                ok_cell = ws_gold is not None and _compare_cell_value(gv, pv)
+                match = "✓" if ok_cell else "✗"
+                # Skip cells that are correct AND empty on both sides: for large
+                # answer ranges (e.g. C2:C5000) the vast majority are empty
+                # (got=None, expected=None ✓) and would otherwise flood the
+                # report with hundreds of thousands of noise chars, burying the
+                # few real ✗ lines. We only emit wrong cells and non-empty
+                # correct cells; empty-correct cells are collapsed into a count.
+                if ok_cell and gv in (None, "") and pv in (None, ""):
+                    n_correct_skipped += 1
+                    continue
                 lines.append(f"  {sheet_name}!{cn}: got={pv!r}, expected={gv!r} {match}")
+            if n_correct_skipped:
+                lines.append(
+                    f"  (+{n_correct_skipped} empty cells correct, omitted)"
+                )
 
         # Also check if any cells in the output contain formula strings
         formula_cells = []