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How to evaluate the frozen controller on a text benchmark with State of Thought

Run the released controller on a fixed text reasoning split with its backbone.

  • Research
  • Advanced
  • 2 hours
  • From the README
  • Any agent

Credentials. Gated backbone weights need a Hugging Face account, license acceptance, and hf auth login on your machine, so keep those tokens out of shared logs and agent transcripts.

Who it is forAn ML researcher who needs to reproduce a text reasoning result with Llama-3.1-8B, Qwen2.5-14B, or Mixtral-8x7B before extending the method.

The problemReproducing a controller-based reasoning result requires matching the backbone, data split, and configuration exactly, which is easy to get wrong by hand.

The outcomeA validated evaluation split and an evaluation summary for the frozen controller, with the configuration, checkpoint identity, and token counts recorded.

What you need first

  • Python with pip and git installed
  • A virtual environment for the install
  • Hugging Face access for backbone weights, with license acceptance and hf auth login for gated models
  • Enough disk space and GPU memory for the chosen backbone when running evaluation

Steps

  1. Run git clone https://github.com/GongZhiren/State-of-Thought.git
  2. Run cd State-of-Thought
  3. Run python -m pip install -e . inside an activated virtual environment
  4. Prepare the evaluation data as described in data/README.md
  5. Run sot data validate --config experiments/paper/llama-3.1-8b.yaml
  6. Run sot evaluate --config experiments/paper/llama-3.1-8b.yaml

Prompt for your agent

In the repo GongZhiren/State-of-Thought, install the package, prepare data as described in data/README.md, then run sot data validate and sot evaluate with experiments/paper/<backbone config>.yaml. Report the validation result and the evaluation summary, and confirm the checkpoint identity recorded in the summary matches the released one.

Caveat. Backbone weights are downloaded separately and are not redistributed, so the run depends on your access to the named Hugging Face model and enough GPU memory for it.

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