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2026-07-13 12:24:16 +08:00

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# ──────────────────────────────────────────────────
# SkillOpt Config Template — <Your Benchmark Name>
# ──────────────────────────────────────────────────
# Copy this file to configs/<your_benchmark>/default.yaml
# and customize the values below.
# Inherit global defaults.
# NOTE: `_base_` is a string path, not a list.
_base_: ../_base_/default.yaml
# ── Environment ──────────────────────────────────
env:
name: your_benchmark # Must match the key registered in scripts/train.py
# Optional: a seed skill document. Create this file yourself before the
# first run, or omit the key to start from an empty skill.
# skill_init: skillopt/envs/your_benchmark/skills/initial.md
data_path: data/your_benchmark # Path to your data (for split_mode: ratio)
split_dir: "" # Set this and use split_mode: split_dir for pre-split data
split_mode: ratio # "ratio" or "split_dir"
split_ratio: "2:1:7" # train:val:test (used when split_mode: ratio)
workers: 4 # Parallel rollout workers
max_completion_tokens: 4096 # Cap per target-model call
limit: 0 # 0 = no limit; small int = debug sample
# ── Training ─────────────────────────────────────
train:
num_epochs: 4
batch_size: 40
accumulation: 1
seed: 42
# ── Gradient (Reflection) ───────────────────────
gradient:
analyst_workers: 16 # Parallel reflection workers
minibatch_size: 8
merge_batch_size: 8
# ── Optimizer ────────────────────────────────────
optimizer:
learning_rate: 4 # Max edits per step (edit budget)
lr_scheduler: cosine # cosine | linear | constant | autonomous
use_slow_update: true # Epoch-boundary momentum
use_meta_skill: true # Cross-epoch optimizer memory
# ── Evaluation ───────────────────────────────────
evaluation:
use_gate: true # Validation gating
eval_test: true # Run test eval after training
# ── Model ────────────────────────────────────────
# Override only what differs from the inherited defaults.
model:
optimizer_backend: openai_chat # openai_chat | claude_chat | qwen_chat | minimax_chat
target_backend: openai_chat # … plus codex_exec / claude_code_exec for target only
reasoning_effort: medium