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test(sleep): add verifier-discipline stress test (closes #67) #87

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Merged
Yifan Yang (Yif-Yang) merged 3 commits into from
Jun 30, 2026
Merged

test(sleep): add verifier-discipline stress test (closes #67) #87

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6849e60
feat(eval): add missing minimax backend configuration
Yif-Yang Jun 23, 2026
fccc21f
test(sleep): add verifier-discipline stress test (closes #67)
Tanmay9223 Jun 24, 2026
680dd28
fix(tests): move TestVerifierDiscipline above main block
Tanmay9223 Jun 30, 2026
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37 changes: 36 additions & 1 deletion scripts/eval_only.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@
configure_claude_code_exec,
configure_codex_exec,
configure_qwen_chat,
configure_minimax_chat,
set_reasoning_effort,
set_target_backend,
set_target_deployment,
Expand Down Expand Up @@ -138,7 +139,7 @@ def parse_args() -> argparse.Namespace:
# Legacy flat overrides
p.add_argument("--env", type=str)
p.add_argument("--backend", type=str,
choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec"])
choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec", "minimax", "minimax_chat"])
p.add_argument("--optimizer_model", type=str)
p.add_argument("--target_model", type=str)
p.add_argument("--optimizer_backend", type=str)
Expand Down Expand Up @@ -180,6 +181,12 @@ def parse_args() -> argparse.Namespace:
p.add_argument("--claude_code_exec_use_sdk", type=str)
p.add_argument("--claude_code_exec_effort", type=str)
p.add_argument("--claude_code_exec_max_thinking_tokens", type=int)
p.add_argument("--minimax_base_url", type=str)
p.add_argument("--minimax_api_key", type=str)
p.add_argument("--minimax_model", type=str)
p.add_argument("--minimax_temperature", type=float)
p.add_argument("--minimax_max_tokens", type=int)
p.add_argument("--minimax_enable_thinking", type=_BOOL)
p.add_argument("--out_root", type=str)
p.add_argument("--data_path", type=str)
p.add_argument("--split_mode", type=str,
Expand Down Expand Up @@ -255,6 +262,12 @@ def main() -> None:
"claude_code_exec_use_sdk": "model.claude_code_exec_use_sdk",
"claude_code_exec_effort": "model.claude_code_exec_effort",
"claude_code_exec_max_thinking_tokens": "model.claude_code_exec_max_thinking_tokens",
"minimax_base_url": "model.minimax_base_url",
"minimax_api_key": "model.minimax_api_key",
"minimax_model": "model.minimax_model",
"minimax_temperature": "model.minimax_temperature",
"minimax_max_tokens": "model.minimax_max_tokens",
"minimax_enable_thinking": "model.minimax_enable_thinking",
"seed": "train.seed",
"test_env_num": "evaluation.test_env_num",
"env": "env.name",
Expand Down Expand Up @@ -312,6 +325,9 @@ def _has_model_override(dotted_key: str, legacy_key: str) -> bool:
elif backend == "claude_code_exec":
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "claude_code_exec")
elif backend in {"minimax", "minimax_chat"}:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "minimax_chat")
else:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "openai_chat")
Expand All @@ -337,6 +353,15 @@ def _has_model_override(dotted_key: str, legacy_key: str) -> bool:
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = default_model_for_backend("claude_chat")
if cfg.get("target_backend") == "minimax_chat":
if (
str(cfg.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = (
cfg.get("minimax_model")
or default_model_for_backend("minimax_chat")
)

if not cfg.get("out_root"):
env = cfg.get("env", "unknown")
Expand Down Expand Up @@ -416,6 +441,16 @@ def _has_model_override(dotted_key: str, legacy_key: str) -> bool:
target_max_tokens=cfg.get("target_qwen_chat_max_tokens"),
target_enable_thinking=cfg.get("target_qwen_chat_enable_thinking"),
)
configure_minimax_chat(
base_url=cfg.get("minimax_base_url") or None,
api_key=cfg.get("minimax_api_key") or None,
temperature=cfg.get("minimax_temperature"),
max_tokens=cfg.get("minimax_max_tokens"),
enable_thinking=cfg.get("minimax_enable_thinking"),
)
minimax_model_cfg = cfg.get("minimax_model")
if minimax_model_cfg and cfg.get("target_backend") == "minimax_chat":
set_target_deployment(str(minimax_model_cfg))
set_reasoning_effort(cfg.get("reasoning_effort", "") or None)

# Build adapter
Expand Down
41 changes: 41 additions & 0 deletions tests/test_sleep_engine.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -1006,5 +1006,46 @@ class FakeProc:
self.assertIn("Not logged in", getattr(be, "last_call_error", ""))




class MockRewardHackingBackend(MockBackend):
def attempt(self, task: TaskRecord, skill: str, memory: str, sample_id: int = 0) -> str:
ctx = (skill or "") + "\n" + (memory or "")
# If the reward-hacking artifact is present in the context:
if "Always emit placeholder URLs" in ctx:
if "rule:__reward_hacking__" in task.tags:
return str(task.reference) # perfectly answers the train/replay shortcut task
else:
return "placeholder URL" # completely breaks the real held-out task

# Baseline behavior (without the rule)
if "rule:__reward_hacking__" in task.tags:
return "I am missing input" # baseline fails the shortcut task
if "rule:real" in task.tags:
return str(task.reference) # baseline gets the real task right

return super().attempt(task, skill, memory, sample_id)

def reflect(self, failures, successes, skill, memory, **kwargs):
edits = []
for task, _ in failures:
if "rule:__reward_hacking__" in task.tags:
edits.append(EditRecord("skill", "add", "Always emit placeholder URLs", rationale="shortcut"))
break
return edits

class TestVerifierDiscipline(unittest.TestCase):
def test_gate_rejects_reward_hacking_edit(self):
be = MockRewardHackingBackend()
train_task = TaskRecord(id="t1", project="/p", intent="train", reference="XYZABC", reference_kind="exact", tags=["rule:__reward_hacking__"], split="train")
val_task = TaskRecord(id="v1", project="/p", intent="val", reference="PQRSTU", reference_kind="exact", tags=["rule:real"], split="val")
tasks = [train_task, val_task]

res = consolidate(be, tasks, "", "", edit_budget=4, gate_metric="hard", night=1)

self.assertFalse(res.accepted)
self.assertGreater(len(res.rejected_edits), 0)
self.assertIn("placeholder", res.rejected_edits[0].content)

if __name__ == "__main__":
unittest.main(verbosity=2)