Configuration Reference

SkillOpt loads structured YAML, resolves _base_ inheritance, and flattens the result for the trainer. Shipped defaults live in configs/_base_/default.yaml; benchmark configs override them.

Model and Backend Selection

Use explicit optimizer and target backends when the two roles differ or when selecting the generic OpenAI-compatible backend.

Backend	Optimizer	Target
openai_chat	✓	✓
openai_compatible	✓	✓
claude_chat	✓	✓
qwen_chat	✓	✓
minimax_chat	✓	✓
codex_exec	✓	✓
claude_code_exec	—	✓
cursor_exec	—	✓

MiniMax currently has one shared deployment. model.minimax_model is applied when MiniMax is the target; mixed-backend runs cannot independently choose a MiniMax optimizer model and a different target model.

Parameter	Type	Default	Description
model.backend	str	azure_openai	Backward-compatible high-level run label
model.optimizer	str	gpt-5.5	Optimizer deployment/model
model.target	str	gpt-5.5	Target deployment/model
model.optimizer_backend	str	openai_chat	Optimizer client path; chat backends plus codex_exec
model.target_backend	str	openai_chat	Target client path; chat or exec backend
model.reasoning_effort	str	medium	Shared reasoning effort
model.rewrite_reasoning_effort	str	empty	Optional full-rewrite effort override
model.rewrite_max_completion_tokens	int	64000	Full-rewrite output cap

Azure/OpenAI openai_chat

Parameter	Default	Description
model.azure_openai_endpoint	empty	Shared Azure resource URL or compatibility-mode base URL
model.azure_openai_api_version	2024-12-01-preview	Azure API version
model.azure_openai_api_key	empty	Key for api_key or compatibility auth
model.azure_openai_auth_mode	empty	Config value; empty falls back to env, whose default is azure_cli
model.azure_openai_ad_scope	Azure Cognitive Services scope	AAD token scope
model.azure_openai_managed_identity_client_id	empty	Optional user-assigned identity client ID

Every shared key also has an optimizer_azure_openai_* and target_azure_openai_* form.

Claude claude_chat

claude_chat launches an installed, authenticated Claude Code CLI with claude -p; it does not instantiate an Anthropic API client. The executable defaults to claude and can be overridden with CLAUDE_CLI_BIN. ANTHROPIC_API_KEY is one authentication option understood by the CLI.

Qwen, MiniMax, and Exec Backends

Parameter family	Description
model.qwen_chat_*	Shared base_url, api_key, temperature, timeout_seconds, max_tokens, and enable_thinking
model.optimizer_qwen_chat_* / model.target_qwen_chat_*	Per-role Qwen overrides
model.minimax_*	MiniMax base_url, api_key, shared minimax_model, temperature, max_tokens, and enable_thinking; minimax_model applies when MiniMax is the target
model.codex_exec_*	Codex path, sandbox, profile, SDK mode, reasoning, network/search, and approval policy
model.claude_code_exec_*	Claude path, profile, SDK mode, effort, and thinking-token cap
model.cursor_exec_path	Cursor Agent executable path; default cursor-agent
model.cursor_exec_sandbox	Cursor sandbox mode: enabled (default) or disabled; file-edit rollouts require enabled

Training (train)

Parameter	Type	Default	Description
train.num_epochs	int	4	Training epochs
train.train_size	int	0	0 derives the size from the dataset split
train.steps_per_epoch	int	derived	Runtime field recomputed from train size, batch size, and accumulation; configured values are overwritten
train.batch_size	int	40	Tasks sampled per step
train.accumulation	int	1	Accumulation rounds per step
train.seed	int	42	Random seed

Gradient / Reflection (gradient)

Parameter	Type	Default	Description
gradient.minibatch_size	int	8	Reflect minibatch size
gradient.merge_batch_size	int	8	Patch merge batch size
gradient.analyst_workers	int	16	Parallel reflection workers
gradient.max_analyst_rounds	int	3	Maximum analyst rounds
gradient.failure_only	bool	false	Reflect only on failures

Optimizer (optimizer)

Parameter	Type	Default	Description
optimizer.learning_rate	int	4	Maximum edit patches per step
optimizer.min_learning_rate	int	2	Floor for decaying schedules
optimizer.lr_scheduler	str	cosine	constant, linear, cosine, or autonomous
optimizer.lr_control_mode	str	fixed	fixed, autonomous, or none
optimizer.skill_update_mode	str	patch	patch, rewrite_from_suggestions, or full_rewrite_minibatch
optimizer.use_slow_update	bool	true	Epoch-boundary longitudinal update
optimizer.slow_update_samples	int	20	Longitudinal evaluation samples
optimizer.slow_update_gate_with_selection	bool	false	Gate slow-update guidance on the selection split
optimizer.longitudinal_pair_policy	str	mixed	mixed, changed, or unchanged
optimizer.use_meta_skill	bool	true	Cross-epoch optimizer memory
optimizer.use_skill_aware_reflection	bool	false	Enable skill-defect vs execution-lapse routing
optimizer.skill_aware_appendix_source	str	both	both or failure_only
optimizer.skill_aware_consolidate_threshold	int	0	Appendix compaction threshold; 0 disables it

Evaluation (evaluation)

Parameter	Type	Default	Description
evaluation.use_gate	bool	true	Accept only improvements when enabled; false records validation but force-accepts each candidate
evaluation.gate_metric	str	hard	hard, soft, or mixed
evaluation.gate_mixed_weight	float	0.5	Soft-score weight for mixed
evaluation.use_semantic_density	bool	false	Add the optional instruction-density bonus
evaluation.semantic_density_weight	float	0.05	Density bonus weight
evaluation.leading_words	list/str	built in	Optional custom high-influence words
evaluation.sel_env_num	int	0	Selection size; 0 uses the full split
evaluation.test_env_num	int	0	Test size; 0 uses the full split
evaluation.eval_test	bool	true	Run final test evaluation

Environment (env)

Parameter	Type	Default	Description
env.name	str	empty	Benchmark name
env.skill_init	str	empty	Initial skill document
env.split_mode	str	ratio	ratio or split_dir
env.split_ratio	str	benchmark/default	Train:validation:test ratio
env.split_seed	int	42	Deterministic split seed
env.split_dir	str	empty	Materialized train/val/test directory
env.data_path	str	empty	Raw data path for ratio mode
env.split_output_dir	str	empty	Optional materialized split output
env.exec_timeout	int	120	Per-task timeout in seconds
env.out_root	str	generated by the train/eval CLIs	Output directory

Benchmark-specific env keys are passed through to the adapter.

Credential Environment Variables

Azure-family backend

Variable	Description
AZURE_OPENAI_ENDPOINT	Shared Azure endpoint or compatibility base URL
AZURE_OPENAI_API_VERSION	Azure API version
AZURE_OPENAI_AUTH_MODE	api_key, azure_cli, managed_identity, or openai_compatible
AZURE_OPENAI_API_KEY	Key for api_key or openai_compatible mode
AZURE_OPENAI_AD_SCOPE	Optional AAD scope
AZURE_OPENAI_MANAGED_IDENTITY_CLIENT_ID	Optional managed-identity client ID

Use OPTIMIZER_AZURE_OPENAI_* and TARGET_AZURE_OPENAI_* for role-specific overrides.

Generic OpenAI-compatible backend

Variable suffix	Shared / per-role forms
BASE_URL	OPENAI_COMPATIBLE_BASE_URL, OPTIMIZER_OPENAI_COMPATIBLE_BASE_URL, TARGET_OPENAI_COMPATIBLE_BASE_URL
API_KEY	Corresponding shared/optimizer/target *_API_KEY names
MODEL	Corresponding shared/optimizer/target *_MODEL names
TEMPERATURE	Corresponding shared/optimizer/target *_TEMPERATURE names
MAX_TOKENS	Corresponding shared/optimizer/target *_MAX_TOKENS names
TIMEOUT_SECONDS	Corresponding shared/optimizer/target *_TIMEOUT_SECONDS names

The train/eval entry points set deployments from YAML model.optimizer and model.target after backend initialization. For selected OpenAI-compatible or Qwen roles, those values override the corresponding *_MODEL environment variables; the environment model names mainly seed direct library use.

Other backend families use the authenticated Claude CLI (CLAUDE_CLI_BIN; optionally ANTHROPIC_API_KEY), QWEN_CHAT_*, and MINIMAX_*. SkillOpt-Sleep's compatible endpoint uses AZURE_OPENAI_*, not the research backend's OPENAI_COMPATIBLE_*; see the Sleep endpoint guide.