"""Lazy-import built-in adapters so we don't pull heavy deps at CLI parse time."""

try:

from skillopt.envs.alfworld.adapter import ALFWorldAdapter

_ENV_REGISTRY["alfworld"] = ALFWorldAdapter

except ImportError:

pass # ALFWorld deps not installed — skip

try:

from skillopt.envs.searchqa.adapter import SearchQAAdapter

_ENV_REGISTRY["searchqa"] = SearchQAAdapter

except ImportError:

pass

try:

from skillopt.envs.livemathematicianbench.adapter import LiveMathematicianBenchAdapter

_ENV_REGISTRY["livemathematicianbench"] = LiveMathematicianBenchAdapter

except ImportError:

pass

try:

from skillopt.envs.babyvision.adapter import BabyVisionAdapter

_ENV_REGISTRY["babyvision"] = BabyVisionAdapter

except ImportError:

pass

try:

from skillopt.envs.spreadsheetbench.adapter import SpreadsheetBenchAdapter

_ENV_REGISTRY["spreadsheetbench"] = SpreadsheetBenchAdapter

except ImportError:

pass

try:

from skillopt.envs.mmrb.adapter import MMRBAdapter

_ENV_REGISTRY["mmrb"] = MMRBAdapter

except ImportError:

pass

try:

from skillopt.envs.docvqa.adapter import DocVQAAdapter

_ENV_REGISTRY["docvqa"] = DocVQAAdapter

except ImportError:

pass

try:

from skillopt.envs.mathverse.adapter import MathVerseAdapter

_ENV_REGISTRY["mathverse"] = MathVerseAdapter

except ImportError:

pass

try:

from skillopt.envs.officeqa.adapter import OfficeQAAdapter

_ENV_REGISTRY["officeqa"] = OfficeQAAdapter

except ImportError:

pass

try:

from skillopt.envs.sealqa.adapter import SealQAAdapter

_ENV_REGISTRY["sealqa"] = SealQAAdapter

except ImportError:

pass

try:

from skillopt.envs.swebench.adapter import SWEBenchAdapter

_ENV_REGISTRY["swebench"] = SWEBenchAdapter

except ImportError:

"""Instantiate the environment adapter specified in ``cfg["env"]``."""

_register_builtins()

env_name = cfg.get("env", "alfworld")

if env_name not in _ENV_REGISTRY:

raise ValueError(

f"Unknown environment '{env_name}'. "

f"Available: {list(_ENV_REGISTRY.keys())}"

)

adapter_cls = _ENV_REGISTRY[env_name]

# Inspect adapter __init__ signature and only pass accepted kwargs

import inspect

sig = inspect.signature(adapter_cls.__init__)

accepted = set(sig.parameters.keys()) - {"self"}

adapter_kwargs: dict = {}

for key in accepted:

if key in cfg:

adapter_kwargs[key] = cfg[key]

p = argparse.ArgumentParser(

description="SkillOpt: Executive Strategy for Self-Evolving Agent Skills",

formatter_class=argparse.RawDescriptionHelpFormatter,

epilog=__doc__,

)

p.add_argument("--config", type=str, required=True,

help="Path to YAML config file")

p.add_argument("--cfg-options", nargs="+", default=[],

help="Override config: section.key=value (e.g. train.batch_size=40)")

# Legacy flat CLI overrides (still work, prefer --cfg-options for new usage)

p.add_argument("--env", type=str)

p.add_argument("--backend", type=str,

choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec", "cursor", "cursor_exec", "copilot", "copilot_chat", "copilot_exec", "qwen", "qwen_chat", "minimax", "minimax_chat"])

p.add_argument("--optimizer_model", type=str)

p.add_argument("--target_model", type=str)

p.add_argument("--optimizer_backend", type=str)

p.add_argument("--target_backend", type=str)

p.add_argument("--reasoning_effort", type=str,

choices=["", "low", "medium", "high", "xhigh", "max"])

p.add_argument("--rewrite_reasoning_effort", type=str)

p.add_argument("--rewrite_max_completion_tokens", type=int)

p.add_argument("--azure_endpoint", type=str)

p.add_argument("--azure_api_version", type=str)

p.add_argument("--azure_api_key", type=str)

p.add_argument("--azure_openai_endpoint", type=str)

p.add_argument("--azure_openai_api_version", type=str)

p.add_argument("--azure_openai_api_key", type=str)

p.add_argument("--azure_openai_auth_mode", type=str)

p.add_argument("--azure_openai_ad_scope", type=str)

p.add_argument("--azure_openai_managed_identity_client_id", type=str)

p.add_argument("--optimizer_azure_openai_endpoint", type=str)

p.add_argument("--optimizer_azure_openai_api_version", type=str)

p.add_argument("--optimizer_azure_openai_api_key", type=str)

p.add_argument("--optimizer_azure_openai_auth_mode", type=str)

p.add_argument("--optimizer_azure_openai_ad_scope", type=str)

p.add_argument("--optimizer_azure_openai_managed_identity_client_id", type=str)

p.add_argument("--target_azure_openai_endpoint", type=str)

p.add_argument("--target_azure_openai_api_version", type=str)

p.add_argument("--target_azure_openai_api_key", type=str)

p.add_argument("--target_azure_openai_auth_mode", type=str)

p.add_argument("--target_azure_openai_ad_scope", type=str)

p.add_argument("--target_azure_openai_managed_identity_client_id", type=str)

p.add_argument("--qwen_chat_base_url", type=str)

p.add_argument("--qwen_chat_api_key", type=str)

p.add_argument("--qwen_chat_temperature", type=float)

p.add_argument("--qwen_chat_timeout_seconds", type=float)

p.add_argument("--qwen_chat_max_tokens", type=int)

p.add_argument("--qwen_chat_enable_thinking", type=_BOOL)

p.add_argument("--qwen_chat_thinking_mode", type=str)

p.add_argument("--optimizer_qwen_chat_base_url", type=str)

p.add_argument("--optimizer_qwen_chat_api_key", type=str)

p.add_argument("--optimizer_qwen_chat_temperature", type=float)

p.add_argument("--optimizer_qwen_chat_timeout_seconds", type=float)

p.add_argument("--optimizer_qwen_chat_max_tokens", type=int)

p.add_argument("--optimizer_qwen_chat_enable_thinking", type=_BOOL)

p.add_argument("--optimizer_qwen_chat_thinking_mode", type=str)

p.add_argument("--target_qwen_chat_base_url", type=str)

p.add_argument("--target_qwen_chat_api_key", type=str)

p.add_argument("--target_qwen_chat_temperature", type=float)

p.add_argument("--target_qwen_chat_timeout_seconds", type=float)

p.add_argument("--target_qwen_chat_max_tokens", type=int)

p.add_argument("--target_qwen_chat_enable_thinking", type=_BOOL)

p.add_argument("--target_qwen_chat_thinking_mode", type=str)

p.add_argument("--minimax_region", type=str)

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("--codex_exec_path", type=str)

p.add_argument("--codex_exec_sandbox", type=str)

p.add_argument("--codex_exec_profile", type=str)

p.add_argument(

"--codex_exec_full_auto",

type=_BOOL,

help=(

"Deprecated and ignored; use --codex_exec_sandbox and "

"--codex_exec_approval_policy"

),

)

p.add_argument("--codex_exec_reasoning_effort", type=str)

p.add_argument("--codex_exec_use_sdk", type=str)

p.add_argument("--codex_exec_network_access", type=_BOOL)

p.add_argument("--codex_exec_web_search", type=_BOOL)

p.add_argument("--codex_exec_approval_policy", type=str)

p.add_argument("--claude_code_exec_path", type=str)

p.add_argument("--claude_code_exec_profile", type=str)

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("--cursor_exec_path", type=str)

p.add_argument("--cursor_exec_sandbox", type=str)

p.add_argument("--copilot_exec_path", type=str)

p.add_argument("--copilot_exec_home", type=str)

p.add_argument("--copilot_exec_allow_all_tools", type=_BOOL)

p.add_argument("--copilot_chat_optimizer_model", type=str)

p.add_argument("--copilot_chat_target_model", type=str)

p.add_argument("--copilot_chat_timeout", type=int)

p.add_argument("--codex_trace_to_optimizer", type=_BOOL)

p.add_argument("--skill_init", type=str)

p.add_argument("--num_epochs", type=int)

p.add_argument("--train_size", type=int)

p.add_argument("--steps_per_epoch", type=int)

p.add_argument("--batch_size", type=int)

p.add_argument("--accumulation", type=int)

p.add_argument("--seed", type=int)

p.add_argument("--edit_budget", type=int)

p.add_argument("--min_edit_budget", type=int)

p.add_argument("--lr_scheduler", type=str,

choices=["constant", "linear", "cosine", "autonomous"])

p.add_argument("--lr_control_mode", type=str,

choices=["fixed", "autonomous", "none"])

p.add_argument("--merge_batch_size", type=int)

# Retired, still accepted so existing launch scripts keep running.

p.add_argument("--max_analyst_rounds", type=int, help=argparse.SUPPRESS)

p.add_argument("--sel_env_num", type=int)

p.add_argument("--test_env_num", type=int)

p.add_argument("--eval_test", type=_BOOL)

p.add_argument("--use_gate", type=_BOOL)

p.add_argument("--max_steps", type=int)

p.add_argument("--max_api_workers", type=int)

p.add_argument("--analyst_workers", type=int)

p.add_argument("--failure_only", type=_BOOL)

p.add_argument("--minibatch_size", type=int)

p.add_argument("--skill_update_mode", type=str,

choices=[

"patch",

"rewrite_from_suggestions",

"rewrite",

"suggestions",

"full_rewrite",

"full_rewrite_minibatch",

"minibatch_full_rewrite",

])

p.add_argument("--use_slow_update", type=_BOOL)

p.add_argument("--slow_update_samples", type=int)

p.add_argument("--longitudinal_pair_policy", type=str,

choices=["mixed", "changed", "unchanged"])

p.add_argument("--use_meta_skill", type=_BOOL)

p.add_argument("--use_skill_aware_reflection", type=_BOOL)

p.add_argument("--skill_aware_appendix_source", type=str,

choices=["both", "failure_only"])

p.add_argument("--skill_aware_consolidate_threshold", type=int)

p.add_argument("--data_path", type=str)

p.add_argument("--split_mode", type=str,

choices=["ratio", "split_dir"])

p.add_argument("--split_ratio", type=str)

p.add_argument("--split_seed", type=int)

p.add_argument("--split_dir", type=str)

p.add_argument("--split_output_dir", type=str)

p.add_argument("--data_root", type=str)

p.add_argument("--max_turns", type=int)

p.add_argument("--workers", type=int)

p.add_argument("--limit", type=int)

p.add_argument("--shuffle_choices", type=_BOOL)

p.add_argument("--use_theorem", type=_BOOL)

p.add_argument("--use_sketch", type=_BOOL)

p.add_argument("--image_detail", type=str)

p.add_argument("--judge_model", type=str)

p.add_argument("--judge_max_completion_tokens", type=int)

p.add_argument("--judge_retries", type=int)

p.add_argument("--out_root", type=str)

p.add_argument("--mode", type=str)

"max_analyst_rounds": (

"gradient.max_analyst_rounds",

"the analyst call count follows from the rollout results, "

"gradient.minibatch_size and gradient.failure_only",

),

"""Value at a dotted path in *cfg*, or ``None`` if any level is missing."""

node: object = cfg

for part in dotted.split("."):

if not isinstance(node, dict) or part not in node:

return None

node = node[part]

args: argparse.Namespace,

cfg: dict,

structured: bool,

flat_name: str,

dotted: str,

) -> list[str]:

"""Every input path that still supplies a retired option.

sources: list[str] = []

if getattr(args, flat_name, None) is not None:

sources.append(f"--{flat_name}")

accepted = {dotted, flat_name}

overrides = [

key.strip()

for key, separator, _value in (

str(option).partition("=")

for option in getattr(args, "cfg_options", None) or []

)

if separator and key.strip() in accepted

]

sources.extend(f"--cfg-options {key}" for key in overrides)

# Only when no override named it: an override is already reported as the

# source and is filtered before loading so it cannot change config format.

if not overrides:

key = dotted if structured else flat_name

if _lookup_dotted(cfg, key) is not None:

sources.append("the config file")

"backend": "model.backend",

"optimizer_model": "model.optimizer",

"target_model": "model.target",

"optimizer_backend": "model.optimizer_backend",

"target_backend": "model.target_backend",

"reasoning_effort": "model.reasoning_effort",

"rewrite_reasoning_effort": "model.rewrite_reasoning_effort",

"rewrite_max_completion_tokens": "model.rewrite_max_completion_tokens",

"azure_endpoint": "model.azure_endpoint",

"azure_api_version": "model.azure_api_version",

"azure_api_key": "model.azure_api_key",

"azure_openai_endpoint": "model.azure_openai_endpoint",

"azure_openai_api_version": "model.azure_openai_api_version",

"azure_openai_api_key": "model.azure_openai_api_key",

"azure_openai_auth_mode": "model.azure_openai_auth_mode",

"azure_openai_ad_scope": "model.azure_openai_ad_scope",

"azure_openai_managed_identity_client_id": "model.azure_openai_managed_identity_client_id",

"optimizer_azure_openai_endpoint": "model.optimizer_azure_openai_endpoint",

"optimizer_azure_openai_api_version": "model.optimizer_azure_openai_api_version",

"optimizer_azure_openai_api_key": "model.optimizer_azure_openai_api_key",

"optimizer_azure_openai_auth_mode": "model.optimizer_azure_openai_auth_mode",

"optimizer_azure_openai_ad_scope": "model.optimizer_azure_openai_ad_scope",

"optimizer_azure_openai_managed_identity_client_id": "model.optimizer_azure_openai_managed_identity_client_id",

"target_azure_openai_endpoint": "model.target_azure_openai_endpoint",

"target_azure_openai_api_version": "model.target_azure_openai_api_version",

"target_azure_openai_api_key": "model.target_azure_openai_api_key",

"target_azure_openai_auth_mode": "model.target_azure_openai_auth_mode",

"target_azure_openai_ad_scope": "model.target_azure_openai_ad_scope",

"target_azure_openai_managed_identity_client_id": "model.target_azure_openai_managed_identity_client_id",

"qwen_chat_base_url": "model.qwen_chat_base_url",

"qwen_chat_api_key": "model.qwen_chat_api_key",

"qwen_chat_temperature": "model.qwen_chat_temperature",

"qwen_chat_timeout_seconds": "model.qwen_chat_timeout_seconds",

"qwen_chat_max_tokens": "model.qwen_chat_max_tokens",

"qwen_chat_enable_thinking": "model.qwen_chat_enable_thinking",

"qwen_chat_thinking_mode": "model.qwen_chat_thinking_mode",

"optimizer_qwen_chat_base_url": "model.optimizer_qwen_chat_base_url",

"optimizer_qwen_chat_api_key": "model.optimizer_qwen_chat_api_key",

"optimizer_qwen_chat_temperature": "model.optimizer_qwen_chat_temperature",

"optimizer_qwen_chat_timeout_seconds": "model.optimizer_qwen_chat_timeout_seconds",

"optimizer_qwen_chat_max_tokens": "model.optimizer_qwen_chat_max_tokens",

"optimizer_qwen_chat_enable_thinking": "model.optimizer_qwen_chat_enable_thinking",

"optimizer_qwen_chat_thinking_mode": "model.optimizer_qwen_chat_thinking_mode",

"target_qwen_chat_base_url": "model.target_qwen_chat_base_url",

"target_qwen_chat_api_key": "model.target_qwen_chat_api_key",

"target_qwen_chat_temperature": "model.target_qwen_chat_temperature",

"target_qwen_chat_timeout_seconds": "model.target_qwen_chat_timeout_seconds",

"target_qwen_chat_max_tokens": "model.target_qwen_chat_max_tokens",

"target_qwen_chat_enable_thinking": "model.target_qwen_chat_enable_thinking",

"target_qwen_chat_thinking_mode": "model.target_qwen_chat_thinking_mode",

"minimax_region": "model.minimax_region",

"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",

"codex_exec_path": "model.codex_exec_path",

"codex_exec_sandbox": "model.codex_exec_sandbox",

"codex_exec_profile": "model.codex_exec_profile",

"codex_exec_full_auto": "model.codex_exec_full_auto",

"codex_exec_reasoning_effort": "model.codex_exec_reasoning_effort",

"codex_exec_use_sdk": "model.codex_exec_use_sdk",

"codex_exec_network_access": "model.codex_exec_network_access",

"codex_exec_web_search": "model.codex_exec_web_search",

"codex_exec_approval_policy": "model.codex_exec_approval_policy",

"claude_code_exec_path": "model.claude_code_exec_path",

"claude_code_exec_profile": "model.claude_code_exec_profile",

"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",

"cursor_exec_path": "model.cursor_exec_path",

"cursor_exec_sandbox": "model.cursor_exec_sandbox",

"copilot_exec_path": "model.copilot_exec_path",

"copilot_exec_home": "model.copilot_exec_home",

"copilot_exec_allow_all_tools": "model.copilot_exec_allow_all_tools",

"copilot_chat_optimizer_model": "model.copilot_chat_optimizer_model",

"copilot_chat_target_model": "model.copilot_chat_target_model",

"copilot_chat_timeout": "model.copilot_chat_timeout",

"codex_trace_to_optimizer": "model.codex_trace_to_optimizer",

"num_epochs": "train.num_epochs",

"train_size": "train.train_size",

"steps_per_epoch": "train.steps_per_epoch",

"batch_size": "train.batch_size",

"accumulation": "train.accumulation",

"seed": "train.seed",

"minibatch_size": "gradient.minibatch_size",

"merge_batch_size": "gradient.merge_batch_size",

"analyst_workers": "gradient.analyst_workers",

"failure_only": "gradient.failure_only",

"edit_budget": "optimizer.learning_rate",

"min_edit_budget": "optimizer.min_learning_rate",

"lr_scheduler": "optimizer.lr_scheduler",

"lr_control_mode": "optimizer.lr_control_mode",

"skill_update_mode": "optimizer.skill_update_mode",

"use_slow_update": "optimizer.use_slow_update",

"slow_update_samples": "optimizer.slow_update_samples",

"longitudinal_pair_policy": "optimizer.longitudinal_pair_policy",

"use_meta_skill": "optimizer.use_meta_skill",

"use_skill_aware_reflection": "optimizer.use_skill_aware_reflection",

"skill_aware_appendix_source": "optimizer.skill_aware_appendix_source",

"skill_aware_consolidate_threshold": "optimizer.skill_aware_consolidate_threshold",

"use_gate": "evaluation.use_gate",

"sel_env_num": "evaluation.sel_env_num",

"test_env_num": "evaluation.test_env_num",

"eval_test": "evaluation.eval_test",

"env": "env.name",

"skill_init": "env.skill_init",

"out_root": "env.out_root",

"""Load config with _base_ inheritance, then apply CLI overrides."""

import warnings

from skillopt.config import load_config as _load, flatten_config, is_structured

# F08: Warn when API keys are supplied on the CLI. Keep the replacement

# guidance specific to each backend and, where applicable, each role.

_credential_guidance = {

"azure_api_key": (

"AZURE_OPENAI_API_KEY or "

"--azure_openai_auth_mode=managed_identity"

),

"azure_openai_api_key": (

"AZURE_OPENAI_API_KEY or "

"--azure_openai_auth_mode=managed_identity"

),

"optimizer_azure_openai_api_key": (

"OPTIMIZER_AZURE_OPENAI_API_KEY or "

"--optimizer_azure_openai_auth_mode=managed_identity"

),

"target_azure_openai_api_key": (

"TARGET_AZURE_OPENAI_API_KEY or "

"--target_azure_openai_auth_mode=managed_identity"

),

"qwen_chat_api_key": "QWEN_CHAT_API_KEY",

"optimizer_qwen_chat_api_key": "OPTIMIZER_QWEN_CHAT_API_KEY",

"target_qwen_chat_api_key": "TARGET_QWEN_CHAT_API_KEY",

"minimax_api_key": "MINIMAX_API_KEY",

}

for _cli_key, _guidance in _credential_guidance.items():

if getattr(args, _cli_key, None):

warnings.warn(

f"--{_cli_key} is deprecated: provide credentials via "

f"{_guidance} instead.",

DeprecationWarning,

stacklevel=2,

)

_structured_credential_guidance = dict(_credential_guidance)

_structured_credential_guidance.update({

# MiniMax is currently configured through one shared runtime client; a

# secret supplied through either role-shaped field belongs in the

# shared MINIMAX_API_KEY environment variable instead.

"optimizer_minimax_api_key": _credential_guidance["minimax_api_key"],

"target_minimax_api_key": _credential_guidance["minimax_api_key"],

})

_credential_suffixes = ("api_key", "api-key", "token", "secret", "password")

for _override in getattr(args, "cfg_options", None) or []:

_key, _separator, _value = str(_override).partition("=")

_key = _key.strip()

_leaf = _key.casefold().rsplit(".", 1)[-1]

_guidance = _structured_credential_guidance.get(_leaf)

if not _guidance and _leaf.endswith(_credential_suffixes):

_guidance = "a backend-specific environment variable or managed identity"

if _separator and _guidance and _value:

warnings.warn(

f"--cfg-options {_key}=... exposes a credential in "

f"the process command line: provide it via {_guidance} instead.",

DeprecationWarning,

stacklevel=2,

)

# Retired dotted overrides can otherwise create a structured section in a

# legacy flat config before format detection. Filter them before loading;

# the original list is still used below to produce the migration warning.

_retired_override_keys = set(_RETIRED_CLI_OPTIONS)

_retired_override_keys.update(

dotted for dotted, _rationale in _RETIRED_CLI_OPTIONS.values()

)

_active_cfg_options = []

for _option in getattr(args, "cfg_options", None) or []:

_key, _separator, _value = str(_option).partition("=")

if _separator and _key.strip() in _retired_override_keys:

continue

_active_cfg_options.append(_option)

cfg = _load(args.config, overrides=_active_cfg_options)

structured = is_structured(cfg)

for _retired, (_dotted, _rationale) in _RETIRED_CLI_OPTIONS.items():

_sources = _retired_option_sources(args, cfg, structured, _retired, _dotted)

if _sources:

warnings.warn(

f"{_dotted} was retired and is ignored: {_rationale}. "

f"Still supplied via {', '.join(_sources)}; remove it.",

# FutureWarning rather than DeprecationWarning: ``skillopt-train``

# is a console script pointing at ``scripts.train:main``, so this

# is raised from an imported module and not from ``__main__``.

# Python's default filters are ``default::DeprecationWarning:

# __main__`` followed by ``ignore::DeprecationWarning``, which

# would drop it before any normal CLI user saw it.

FutureWarning,

stacklevel=2,

)

# A retired key in a legacy flat file would otherwise survive in the

# returned trainer config. Structured files need the nested key

# removed before flattening as well.

cfg.pop(_retired, None)

_section, _key = _dotted.split(".", 1)

_section_cfg = cfg.get(_section)

if isinstance(_section_cfg, dict):

_section_cfg.pop(_key, None)

# Apply legacy --key value overrides

cli = {k: v for k, v in vars(args).items()

if v is not None

and k not in ("config", "cfg_options")

and k not in _RETIRED_CLI_OPTIONS}

if cli:

if structured:

from skillopt.config import apply_overrides

mapped = []

for k, v in cli.items():

dotted = _LEGACY_TO_STRUCTURED.get(k)

if dotted:

mapped.append(f"{dotted}={v}")

else:

mapped.append(f"env.{k}={v}")

apply_overrides(cfg, mapped)

else:

cfg.update(cli)

# Flatten structured config → flat dict for trainer/adapter

flat = flatten_config(cfg) if structured else cfg

for new_key, old_key in (

("azure_openai_endpoint", "azure_endpoint"),

("azure_openai_api_version", "azure_api_version"),

("azure_openai_api_key", "azure_api_key"),

):

if flat.get(new_key) in (None, "") and flat.get(old_key) not in (None, ""):

flat[new_key] = flat[old_key]

explicit_backend = getattr(args, "backend", None)

if explicit_backend is None:

for option in args.cfg_options or []:

key = str(option).split("=", 1)[0].strip()

if key == "model.backend":

explicit_backend = str(option).split("=", 1)[1].strip()

break

if explicit_backend is None:

# ``model.backend`` from a structured YAML file is flattened to

# ``model_backend``. Treat it like the equivalent CLI selection so

# its role mapping (and backend-specific model defaults) is applied.

explicit_backend = flat.get("model_backend") or flat.get("backend")

backend = normalize_backend_name(flat.get("model_backend") or flat.get("target_backend") or "azure_openai")

def _has_model_override(dotted_key: str, legacy_key: str) -> bool:

if getattr(args, legacy_key, None) is not None:

return True

for option in args.cfg_options or []:

key = str(option).split("=", 1)[0].strip()

if key == dotted_key:

return True

return False

if explicit_backend is not None:

backend = normalize_backend_name(explicit_backend)

flat["model_backend"] = backend

def _set_role(key: str, value: str) -> None:

if (

not _has_model_override(f"model.{key}", key)

and flat.get(key) in _ROLE_BACKEND_DEFAULTS

):

flat[key] = value

if backend == "claude_chat":

_set_role("optimizer_backend", "claude_chat")

_set_role("target_backend", "claude_chat")

elif backend in {"codex", "codex_exec"}:

_set_role("optimizer_backend", "codex_exec")

_set_role("target_backend", "codex_exec")

elif backend == "claude_code_exec":

# Only the target defaults to Claude Code (it produces the SDK trace

# the reflector consumes); the optimizer keeps its configured

# backend so an explicit --optimizer_backend is never clobbered.

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "claude_code_exec")

elif backend == "cursor_exec":

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "cursor_exec")

elif backend == "copilot_chat":

_set_role("optimizer_backend", "copilot_chat")

_set_role("target_backend", "copilot_chat")

elif backend == "copilot_exec":

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "copilot_exec")

elif backend == "qwen_chat":

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "qwen_chat")

elif backend == "minimax_chat":

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "minimax_chat")

elif backend == "openai_compatible":

_set_role("optimizer_backend", "openai_compatible")

_set_role("target_backend", "openai_compatible")

else:

_set_role("optimizer_backend", "openai_chat")

_set_role("target_backend", "openai_chat")

else:

flat.setdefault("optimizer_backend", "openai_chat")

flat.setdefault("target_backend", "openai_chat")

if flat.get("optimizer_backend") == "claude_chat":

if (

str(flat.get("optimizer_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.optimizer", "optimizer_model")

):

flat["optimizer_model"] = default_model_for_backend("claude_chat")

if flat.get("optimizer_backend") == "claude_code_exec":

if (

str(flat.get("optimizer_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.optimizer", "optimizer_model")

):

flat["optimizer_model"] = default_model_for_backend("claude_code_exec")

if flat.get("optimizer_backend") == "qwen_chat":

if (

str(flat.get("optimizer_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.optimizer", "optimizer_model")

):

flat["optimizer_model"] = default_model_for_backend("qwen_chat")

if flat.get("target_backend") == "claude_chat":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

flat["target_model"] = default_model_for_backend("claude_chat")

if flat.get("target_backend") == "claude_code_exec":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

flat["target_model"] = default_model_for_backend("claude_chat")

if flat.get("target_backend") == "cursor_exec":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

flat["target_model"] = default_model_for_backend("cursor_exec")

if flat.get("target_backend") == "copilot_exec":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

# Copilot CLI model IDs are independent of Azure deployment names.

flat["target_model"] = ""

if flat.get("target_backend") == "qwen_chat":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

flat["target_model"] = default_model_for_backend("qwen_chat")

if flat.get("target_backend") == "minimax_chat":

if (

str(flat.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS

and not _has_model_override("model.target", "target_model")

):

flat["target_model"] = (

flat.get("minimax_model")

or default_model_for_backend("minimax_chat")

)

# Auto-generate output root

if not flat.get("out_root"):

env = flat.get("env", "unknown")

model = flat.get("optimizer_model", "unknown").replace("/", "-")

ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")

flat["out_root"] = os.path.join("outputs", f"skillopt_{env}_{model}_{ts}")

flat["out_root"] = os.path.abspath(flat["out_root"])

args = parse_args()

cfg = load_config(args)

print(f"\n{'='*60}")

print(f" SkillOpt — Executive Strategy for Self-Evolving Agent Skills")

print(f"{'='*60}")

print(f" env: {cfg.get('env')}")

print(f" optimizer_model: {cfg.get('optimizer_model')}")

print(f" target_model: {cfg.get('target_model')}")

print(f" optimizer_backend:{cfg.get('optimizer_backend', 'openai_chat')}")

print(f" target_backend:{cfg.get('target_backend', 'openai_chat')}")

print(f" reasoning: {cfg.get('reasoning_effort') or 'off'}")

print(f" rewrite_effort: {cfg.get('rewrite_reasoning_effort') or 'off'}")

print(f" epochs: {cfg.get('num_epochs')}")

print(f" train_size: {cfg.get('train_size') or 'from dataset'}")

print(f" steps/epoch: auto")

print(f" batch_size: {cfg.get('batch_size')}")

print(f" edit_budget: {cfg.get('edit_budget')}")

print(f" lr_scheduler: {cfg.get('lr_scheduler', 'constant')}")

print(f" update_mode: {cfg.get('skill_update_mode', 'patch')}")

print(f" min_edit_budget:{cfg.get('min_edit_budget', 2)}")

print(f" minibatch_size: {cfg.get('minibatch_size')}")

print(f" seed: {cfg.get('seed')}")

print(f" meta_skill: {cfg.get('use_meta_skill', False)}")

print(f" skill_aware_reflection: {cfg.get('use_skill_aware_reflection', False)}")

print(f" slow_update: {cfg.get('use_slow_update', False)}")

print(f" out_root: {cfg.get('out_root')}")

print(f"{'='*60}\n")

# Build adapter

adapter = get_adapter(cfg)

# Build trainer and run

from skillopt.engine.trainer import ReflACTTrainer

trainer = ReflACTTrainer(cfg, adapter)

summary = trainer.train()

print(f"\n Output saved to: {cfg['out_root']}")

if summary.get("test_hard") is not None: