microsoft-SkillOpt/docs/guide/configuration.md

Configuration Guide

SkillOpt uses YAML configuration files with a hierarchical override system.

Config Structure

configs/
├── _base_/
│   └── default.yaml          # Global defaults
├── searchqa/
│   └── default.yaml          # SearchQA overrides
├── docvqa/
│   └── default.yaml          # DocVQA overrides
└── alfworld/
    └── default.yaml          # ALFWorld overrides

Benchmark configs inherit from _base_/default.yaml and override specific values.

Key Parameters

Model

model:
  backend: azure_openai          # azure_openai | openai_chat | claude_code_exec | qwen
  optimizer: gpt-5.5               # Optimizer model (for reflection)
  target: gpt-5.5               # Target model (for rollout)

Training

train:
  num_epochs: 4                  # Number of training epochs
  batch_size: 40                 # Tasks per step (batch size)
  accumulation: 1                # Gradient accumulation
  seed: 42

Gradient (Reflection)

gradient:
  minibatch_size: 8              # Reflect minibatch size
  analyst_workers: 16            # Parallel reflection workers
  max_analyst_rounds: 3          # Max rounds of analyst reflection
  failure_only: false            # Only reflect on failures

Optimizer

optimizer:
  learning_rate: 4               # Max edits per step (edit budget)
  min_learning_rate: 2           # Min edits for decay schedulers
  lr_scheduler: cosine           # constant | linear | cosine | autonomous
  use_slow_update: true          # Momentum-like blending at epoch boundary
  slow_update_samples: 20        # Samples for slow update evaluation
  use_meta_skill: true           # Cross-epoch strategy memory

Skill-Aware Reflection (optional, off by default)

EmbodiSkill-style failure routing: the failure analyst classifies each failure pattern as SKILL_DEFECT (the rule is wrong or missing → normal gated body edit) or EXECUTION_LAPSE (a valid rule exists but was not followed → a short reminder appended to a protected appendix region inside the skill that step-level edits can never modify).

optimizer:
  use_skill_aware_reflection: false    # Master switch (default off = baseline-identical)
  skill_aware_appendix_source: both    # both | failure_only (paper-faithful S_app)
  skill_aware_consolidate_threshold: 0 # >0: LLM-compact the appendix past N notes (experimental)

Notes:

• The switch is resolved process-wide from the config (configure_skill_aware_reflection), so it applies to every benchmark with no per-adapter wiring.
• failure_only restricts appendix notes to the failure analyst, matching the original S_app formulation; both additionally lets the success analyst re-emphasize existing rules.
• Appendix notes bypass the validation gate by design and accumulate with order-preserving dedup; lapse-only steps (no body edits) still flush their notes.
• Not supported together with skill_update_mode=rewrite_from_suggestions or the full-rewrite modes: whole-document rewrites can drop the appendix region.

Evaluation

evaluation:
  use_gate: true                 # Validation gating (accept/reject updates)
  eval_test: true                # Run test evaluation after training

Environment (Data)

env:
  name: searchqa                 # Benchmark name
  split_mode: ratio              # ratio | split_dir
  split_ratio: "2:1:7"           # train:val:test ratio
  data_path: ""                  # Path to dataset
  exec_timeout: 120              # Per-task timeout (seconds)

CLI Overrides

Override any config value from the command line:

python scripts/train.py \
  --config configs/searchqa/default.yaml \
  optimizer.learning_rate=16 \
  optimizer.lr_scheduler=linear \
  gradient.analyst_workers=8

Environment Variables

Model credentials are loaded from environment variables:

Variable	Backend	Description
AZURE_OPENAI_ENDPOINT	azure_openai	Azure resource endpoint
AZURE_OPENAI_API_KEY	azure_openai	Azure API key
OPENAI_API_KEY	openai	OpenAI API key
ANTHROPIC_API_KEY	claude	Anthropic API key
QWEN_API_BASE	qwen	Local Qwen vLLM endpoint

Full Reference

See Configuration Reference for the complete parameter list.