add-eval-dataset-config

name	add-eval-dataset-config
description	Guide for adding and validating evaluation dataset configuration in slime. Use when user wants to configure eval datasets via --eval-config or --eval-prompt-data, add per-dataset overrides, or customize evaluation rollout behavior.

Add Eval Dataset Config

Configure evaluation datasets in slime with explicit dataset-level overrides and predictable runtime behavior.

When to Use

Use this skill when:

• User asks to add evaluation datasets for periodic eval
• User asks to migrate from --eval-prompt-data to structured --eval-config
• User asks for per-dataset eval overrides (sampling params, keys, rm_type, metadata)

Step-by-Step Guide

Step 1: Choose Config Entry Method

Supported inputs:

• Structured config file: --eval-config <yaml>
• Legacy CLI pairs: --eval-prompt-data <name1> <path1> <name2> <path2> ...

If --eval-interval is set, eval datasets must be configured.

Step 2: Build YAML with Required Fields

Each dataset needs at least:

• name
• path

Example:

eval:
  defaults:
    n_samples_per_eval_prompt: 1
    temperature: 0.7
    top_p: 1.0
  datasets:
    - name: aime
      path: /path/to/aime.jsonl
      rm_type: math
      input_key: prompt
      label_key: answer
      metadata_overrides:
        split: test

Step 3: Understand Override Priority

slime/utils/eval_config.py resolves fields in this order:

1. Dataset-level values in eval.datasets[*]
2. eval.defaults
3. CLI args fallback (for example eval_* or rollout_* fields)

Common overridable fields include:

• Runtime: n_samples_per_eval_prompt, temperature, top_p, top_k, max_response_len
• Sample keys: input_key, label_key, tool_key, metadata_key
• Extra: rm_type, custom_generate_function_path, metadata_overrides

Step 4: Wire Eval Function if Needed

By default, eval uses --eval-function-path (defaults to rollout function path). Use a separate eval function when inference/eval behavior must differ from training rollout.

Step 5: Validate Parsing and Runtime

• Start with config parsing sanity by running a short launch command.
• Confirm dataset entries are loaded into args.eval_datasets.
• Verify output keys match eval logging/metrics expectations.

Common Mistakes

• Missing name in dataset entries
• Odd-length --eval-prompt-data pairs
• Setting --eval-interval without any eval dataset
• Mixing reward dict outputs without eval_reward_key configuration

Reference Locations

• Eval config model: slime/utils/eval_config.py
• Eval config resolution: slime/utils/arguments.py
• Eval rollout path: slime/rollout/sglang_rollout.py
• Customization docs: docs/en/get_started/customization.md