README.md

---

license: other
pipeline_tag: text-classification
library_name: transformers
base_model: microsoft/deberta-v3-large
tags:
  - text-classification
  - process-reward-model
  - reasoning-verification
  - step-verification
  - crp
  - context-relay-protocol
datasets:
  - trl-lib/prm800k
  - peiyi9979/Math-Shepherd
  - UW-Madison-Lee-Lab/MMLU-Pro-CoT-Train-Labeled
  - RLHFlow/Mistral-PRM-Data
metrics:
  - accuracy
  - auc
model-index:
  - name: crp-prm-deberta-v1
    results:
      - task:
          type: text-classification
          name: Reasoning-step validity (VALID/INVALID)
        dataset:
          type: trl-lib/prm800k
          name: prm800k held-out test steps
        metrics:
          - type: auc
            value: 0.793
            name: ROC AUC (held-out, independent harness)
---


# CRP PRM DeBERTa — process reward model (step verifier)

Part of the [Context Relay Protocol (CRP)](https://crprotocol.io) ML-first
governance layer. Scores whether a reasoning step is entailed by / consistent
with its premises — `VALID` or `INVALID` — inside the CRP Verification Relay
(`crp/vr/prm.py`, SPEC-049). Input format:

```

premises: <problem and prior steps> [SEP] step: <current step>

```

## Verified results (independent harness, 2026-08-01)

| Metric | Value |
|---|---|
| ROC AUC (389 held-out prm800k steps) | **0.793** |
| Curated reasoning cases (mixed domains) | **8/10** |
| VALID recall (threshold ≥ 0.15) | **1.000** — never false-flags good steps |
| Best operating point (this slice) | t=0.1 → INVALID recall 0.577 / VALID recall 0.890 |

Trainer-side eval (on a training-matched mix): eval_loss 0.133, INVALID F1

0.977. See *Calibration* below before using thresholds.



## Intended use — advisory scorer



Wire as an **advisory** step-quality score: it never false-halts good steps

(VALID recall 1.0) and catches blatant bad ones, but subtle math-domain

errors score low (score compression on out-of-training distribution). Hard

INVALID gating should stay with symbolic verifiers / human checkpoints —

exactly how `crp/vr/prm.py` consumes it.



## Calibration (important)



The config's exported `prm_threshold` (0.675) was calibrated on a mix that
includes the same synthetic patterns as training, and **does not transfer to

real distributions** — on pure prm800k test steps, P(INVALID) compresses
below 0.5 (mean 0.098 on true INVALID vs 0.053 on true VALID; ranking is
real, absolute calibration is distribution-dependent). Recalibrate on your
own traffic before thresholding, or use argmax (0.5).

## Training data

~400k step-level examples (stratified ~50/50): `trl-lib/prm800k` (human
labels), `peiyi9979/Math-Shepherd` (GPT-4 step labels),
`UW-Madison-Lee-Lab/MMLU-Pro-CoT-Train-Labeled` (14 non-math domains),
`RLHFlow/Mistral-PRM-Data`, plus 20k synthetic agentic (devops / compliance /
code) reasoning examples. DeBERTa-v3-large, bf16, 2 epochs, lr 1e-5,
RunPod RTX 4090.

## Limitations

Not a substitute for formal/symbolic verification. Weakest on long, subtle
multi-step math derivations (premises truncation at 384 tokens). English
only. Scores are distribution-sensitive — see Calibration.

## License

Elastic License 2.0 — see the CRP repository for details.