experiment:
    project: "your_project"  # Output directory for evaluation results

rollout:
    tensor_parallel_size: 1 # set to 1 by default, if oom, try reduce max_active first, if still oom, set tensor_parallel_size to 8
    max_active: 256
    num_task_per_step: 1
    num_response_per_task: 1
    temperature: 0.1
    max_token: 2000
    block_size: 4
    denoising_steps_per_block: 4
    top_p: 1.0
    top_k: 0
    remasking_strategy: "low_confidence_static" #"low_confidence_static""low_confidence_dynamic"
    dynamic_threshold: 0.9 # no use for "low_confidence_static"
    start_with_think: False
    random_init_ratio: 0.0
    # Batch processing: split dataset into batches to avoid KV cache exhaustion
    # If None or 0, process all prompts at once (original behavior)
    # If set to a positive number, process prompts in batches of this size
    batch_size: 50  # e.g., set to 50 to process 50 prompts at a time in case we run out of KV cache space

evaluation:
    # Model checkpoint to evaluate (overrides 'model' above if specified)
    checkpoint_path: "/path/to/your/checkpoint"
    # Dataset configuration
    # eval_dataset: "MATH_train"    # Dataset name (must exist in data/{eval_dataset}.json)
    eval_dataset: "your_dataset"
    data_type: "math"          # Task type: "math", "code", or "option"
    
    # Distributed evaluation (for multi-node setups)
    num_node: 1                # Total number of nodes
    node_index: 0              # Current node index (0-indexed)
    
    # Output options
    output_unmasking_history: true  # Save step-by-step unmasking history