---
license: apache-2.0
base_model:
  - perplexity-ai/pplx-decider-v1.2
  - Qwen/Qwen3.8-27B
base_model_relation: finetune
library_name: pytorch
pipeline_tag: text-classification
tags:
  - decision-model
  - classification
  - custom-code
  - noncausal
  - joint-questions
  - sn92
---

# REAX 1

REAX 1 is a 27B decision model by REAX (decisionmodels.io). It reads a state and several questions
(choice, yes/no, score) in one forward pass and returns calibrated probabilities without generating text.
It is a fine-tune of **pplx-decider-v1.2** (Perplexity AI, Apache-2.0), itself derived from
Qwen/Qwen3.8-27B (Apache-2.0). This model is not affiliated with or endorsed by Perplexity AI or the Qwen team.

## Intended use
Routing, triage, moderation, grading, retrieval/tool reranking and other structured judgements where the answer is one of a closed set.
Intended as a base for further fine-tuning; it is the base model of the SN92 (REAX) generalist miner competition.
Not for sole decision-making in high-stakes settings (medical, legal, credit, employment) without human review.

## Protocol
Same joint request schema and readout as pplx-decider-v1.2 (joint protocol v3, template `joint-question-inline-readout-v1`):
```
{"state": str|dict|list, "questions": {"<id>": {"type": "choice"|"noul"|"score", "instructions": str|None, "criteria": ...}}, "images": [...]}
```
Full attention is **noncausal**: every question attends to the state and to all other questions. One readout per question at the
`<|fim_suffix|>` marker via the separate readout head ([255, 5120]); probabilities use softmax over option codes A, B, ... with
temperature `1.9754` (`decision_config.json`). The readout head is stored pre-scaled (x1.41 x 1.4) and the saved temperature is scaled
by the same factor, so native probabilities are unchanged; the scaling calibrates the single-question SN92 REAX readout, which uses a
fixed temperature of 2 and ignores `decision_config.json`.
Group questions that are facets of one decision; send independent candidates one per request (see upstream README, "When to group questions").

Loading uses the `pplx_decider` package bundled in `source/src/` (**code by Perplexity AI, Apache-2.0, unmodified**):
```python
import sys; from pathlib import Path
from huggingface_hub import snapshot_download
ckpt = Path(snapshot_download("decisionmodels/reax-1"))
sys.path.insert(0, str(ckpt / "source" / "src"))
from pplx_decider import Decider
decider = Decider.from_pretrained(ckpt, device="cuda")   # ~52 GiB BF16
print(decider.decide({"state": "...", "questions": {"q": {"type": "noul", "instructions": "..."}}}))
```

## Training
Four LoRA fine-tunes of pplx-decider-v1.2 (rank 16/16/64/16, all linear layers, readout head trained, KL-to-base 0.5, lr 1e-4,
1 epoch) were merged into the backbone with weight 1/3 each (a "model soup" of fine-tune deltas). Two of them were trained on decision
questions the base model got wrong or was unsure about ("hard-mined", plus 25 % easy replay); the third on an 8,000-item mix of
generated items and public SN92 specialist training rows (CC-BY-4.0); a fourth on 3,000 Aegis 2.0 training rows (safety classification). Generated items were written by API models and checked by
blind solvers from other model families; during an API outage about 1,400 items of one training file had weaker checks (sometimes
a single verifier or one from the author's family). All items were deduplicated against every benchmark item we hold. No official
benchmark test items were used for training.

## Evaluation (internal, not official)
Our composite validator (frozen eval v4: 6,842 items / 21,896 questions; public benchmark items for evaluation only + private
held-out items written for this purpose; temperature fitted per model):

| Part | pplx-decider-v1.2 | REAX 1 |
|---|---:|---:|
| HF-DI-style public items (5 areas) | 75.26 | 74.48 |
| AA-DI-style public items (Omniscience, HLE-style, When2Call, WildGuard-style) | 61.96 | 64.82 |
| JevBench public items | 92.34 | 93.28 |
| Private same-skill items | 88.40 | 89.78 |
| Private new-domain items | 72.21 | 75.24 |
| **Composite** | **79.76** | **81.39** (+1.63; bootstrap mean +1.60, SE 0.38) |

Question-level accuracy on the private new-domain items: 73.0 % -> 76.0 %. AA-Omniscience-style knowledge with an
"I don't know" option: AA-decision score 30.4 -> 39.4 (the model abstains more often when unsure; head-entity abstention 7.6 %).
Safety classification on 1,713 fresh Aegis 2.0 test rows (never used for selection): 83.9 % -> 84.2 % (21 better / 14 worse).
An earlier soup without the safety fine-tune (same three other components) was audited once on our locked split: composite +1.65 (SE 0.58). These are our own measurements, not official leaderboard results. The model is not ranked on JevBench (SN92 lineage).

## Limitations
- Same input limits and protocol as the base (see upstream README): trained at <= ~12k tokens per request here, base trained at 32k.
- Lower accuracy on arts/taste-style public benchmarks than the base (-1.8 points on that area).
- SN92 REAX prompt (one question, fixed temperature 2): revision ea9d5d8b (head x1.41) scored 2.5 points below the base on the competition's
  calibration draws because it was underconfident there; this revision scales the head by a further 1.4 for that path (native output unchanged).
  On our pool-like dev split this moves it from -2.2 to +1.3 points (category mean, internal measurement).
- Abstains ("I don't know") more often than the base on hard knowledge questions; tune the temperature or the abstain option if a
  task needs forced answers.

## Licence and attribution
Apache-2.0. Based on pplx-decider-v1.2 by Perplexity AI and Qwen3.8-27B by the Qwen team; see NOTICE and MODIFICATIONS.md.
Not affiliated with, endorsed by, or sponsored by Perplexity AI or the Qwen team.