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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.