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myzleo/playpen-prm-9b
playpen-prm-9b is a machine learning model from myzleo. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This repository packages the current LM Playpen submission artifacts for a Qwen3.5-9B policy evaluated with a trained Qwen3.5-9B process reward model (PRM) and four PRM-guided search methods.
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Updated Jul 7, 2026
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From the Hugging Face model README
This repository packages the current LM Playpen submission artifacts for a Qwen3.5-9B policy evaluated with a trained Qwen3.5-9B process reward model (PRM) and four PRM-guided search methods.
prm_checkpoint/: trained 9B PRM adapter checkpoint used for scoring
candidate assistant actions.base_policy_qwen35_9b/: Qwen3.5-9B base policy weights used for
evaluation.code/examples_trl/: PRM training, inference, evaluation, and score export
scripts.code/playpen_search/: implementations of the four search methods:
best-of-N, beam search, DVTS, and lookahead.scripts/: terminal-run shell entrypoints used for local reproduction.eval/: final Playpen .val.json files and score manifests.configs/: example model registry for Playpen reproduction.MODEL_CARD.md: training card and reproducibility details.SUBMISSION_MANIFEST.json: machine-readable summary of the package.UPLOAD_TO_HF.md: commands for creating and uploading a Hugging Face repo.The policy model is Qwen3.5-9B. This package now includes a local copy of the full base policy weights in
base_policy_qwen35_9b/.
The official shared-task README asks for full, merged model weights. Because this system is a composed inference-time method rather than a single merged policy checkpoint, the form submission should explicitly state that the submission consists of:
Qwen3.5-9B policy + trained Qwen3.5-9B PRM adapter + PRM-guided search code
Final standard Playpen score files are in eval/.
| Search method | Clemscore | Statscore | File |
|---|---|---|---|
| best-of-N | 40.85 | 71.88 | eval/Qwen3.5-9B-9B-PRM-best_of_n.val.json |
| beam search | 35.45 | 71.88 | eval/Qwen3.5-9B-9B-PRM-beam.val.json |
| DVTS | 35.48 | 71.88 | eval/Qwen3.5-9B-9B-PRM-dvts.val.json |
| lookahead | 28.21 | 71.88 | eval/Qwen3.5-9B-9B-PRM-lookahead.val.json |
The strongest current submission candidate is best-of-N.
Install and configure Playpen as described by the official repository. Then
copy or adapt configs/model_registry.json into the Playpen checkout so that
Qwen3.5-9B-local-4bit points to this repository's
base_policy_qwen35_9b/ directory. Run the evaluation script directly from a
terminal:
cd /path/to/playpen
PLAYPEN_ROOT=$PWD \
SUBMISSION_ROOT=/path/to/playpen-prm-9b \
CUDA_VISIBLE_DEVICES=0 \
bash /path/to/playpen-prm-9b/scripts/run_9b_prm_search_eval.sh
The script expects a Playpen checkout with the required validation data, clembench games, and model registry. It runs the four search methods sequentially and does not use cost-aware sharding or a worker pool. The data sharding used during the original experiment was only an acceleration mechanism.
The base policy weights are fully packaged. The PRM checkpoint in
prm_checkpoint/ is still an adapter checkpoint. If the organizers require a
single monolithic PRM checkpoint, merge the adapter into a compatible
Qwen3.5-9B sequence-classification PRM backbone before uploading, or ask the
organizers whether PRM adapter + base model reference is acceptable for
composed search submissions.