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>
This commit is contained in:
Cuzyoung
+40
-11
@@ -24,7 +24,7 @@ from collections import defaultdict
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from skillopt.datasets.base import BatchSpec
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from skillopt.envs.base import EnvAdapter
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from skillopt.evaluation.gate import evaluate_gate, select_gate_score
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from skillopt.evaluation.gate import GateResult, evaluate_gate, select_gate_score
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from skillopt.gradient.aggregate import merge_patches
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from skillopt.optimizer.meta_skill import run_meta_skill
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from skillopt.optimizer.clip import rank_and_select
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@@ -467,7 +467,7 @@ def _format_step_buffer(buffer: list[dict]) -> str:
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# Failure patterns
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for p in entry.get("failure_patterns", []):
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ids = ", ".join(p["task_ids"][:3])
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ids = ", ".join(p["task_ids"])
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parts.append(f' - "{p["pattern"]}" (×{p["count"]}, tasks: {ids})')
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# Rejected edits (only present on reject)
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@@ -484,7 +484,7 @@ def _format_step_buffer(buffer: list[dict]) -> str:
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content = e.get("content", "")
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target = e.get("target", "")
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if target:
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parts.append(f' {i}. [{op}] target="{target[:80]}" → "{content}"')
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parts.append(f' {i}. [{op}] target="{target}" → "{content}"')
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else:
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parts.append(f' {i}. [{op}] "{content}"')
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else:
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@@ -863,11 +863,10 @@ class ReflACTTrainer:
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sel_cache[sh] = (rec["selection_hard"], rec["selection_soft"])
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# ── Baseline evaluation on selection set ─────────────────────────
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if cfg.get("use_gate") is False:
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raise ValueError(
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"Gate validation is mandatory in this branch. Remove "
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"`evaluation.use_gate=false` from the config."
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)
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# `use_gate=False` keeps validation running (selection rollout +
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# scoring are unconditional below) but force-accepts every candidate
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# instead of gating it; final skill is chosen manually afterwards.
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use_gate = cfg.get("use_gate", True) is not False
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gate_metric = str(cfg.get("gate_metric", "hard")).strip().lower()
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if gate_metric not in {"hard", "soft", "mixed"}:
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raise ValueError(
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@@ -887,6 +886,8 @@ class ReflACTTrainer:
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if gate_metric == "mixed"
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else ""
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)
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+ ("" if use_gate
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else " (DISABLED → validation runs, candidates force-accepted)")
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)
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slow_gate_with_selection = bool(
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cfg.get("slow_update_gate_with_selection", False)
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@@ -1346,10 +1347,31 @@ class ReflACTTrainer:
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cand_soft=cand_soft,
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metric=gate_metric,
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mixed_weight=gate_mixed_weight,
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)
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) if use_gate else None
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cand_gate_score = select_gate_score(
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cand_hard, cand_soft, gate_metric, gate_mixed_weight,
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)
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if not use_gate:
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# Validation ran (scores recorded above) but the gate is
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# disabled: force-accept the candidate as the new current
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# skill. Best-so-far is still tracked for convenience; the
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# final skill is selected manually from the trajectory.
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if cand_gate_score > best_score:
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fa_best_skill = candidate_skill
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fa_best_score = cand_gate_score
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fa_best_step = global_step
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else:
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fa_best_skill = best_skill
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fa_best_score = best_score
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fa_best_step = best_step
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gate = GateResult(
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action="force_accept",
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current_skill=candidate_skill,
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current_score=cand_gate_score,
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best_skill=fa_best_skill,
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best_score=fa_best_score,
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best_step=fa_best_step,
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)
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step_rec["gate_metric"] = gate_metric
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step_rec["candidate_gate_score"] = cand_gate_score
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step_rec["action"] = gate.action
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@@ -1360,9 +1382,11 @@ class ReflACTTrainer:
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best_skill = gate.best_skill
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best_score = gate.best_score
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best_step = gate.best_step
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if gate.action in {"accept", "accept_new_best"}:
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if gate.action in {"accept", "accept_new_best", "force_accept"}:
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current_origin = f"step_{global_step:04d}"
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if gate.action == "accept_new_best":
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if gate.action == "accept_new_best" or (
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gate.action == "force_accept" and best_step == global_step
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):
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best_origin = current_origin
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if gate_metric == "hard":
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@@ -1384,6 +1408,11 @@ class ReflACTTrainer:
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f" [6/6 EVALUATE] ACCEPT "
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f"{score_label} > current={prev_current:.4f}"
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)
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elif gate.action == "force_accept":
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print(
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f" [6/6 EVALUATE] FORCE-ACCEPT (gate disabled) "
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f"{score_label}"
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)
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else:
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print(
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f" [6/6 EVALUATE] REJECT "
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