Atla Selene Mini: A General Purpose Evaluation Model
Authors: name: Andrei Alexandru, name: Antonia Calvi, name: Henry Broomfield, name: Jackson Golden, name: Kyle Dai, name: Mathias Leys, name: Maurice Burger, name: Max Bartolo, name: Roman Engeler, name: Sashank Pisupati, name: Toby Drane, name: Young Sun Park

We introduce Atla Selene Mini, a state-of-the-art small language
model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that
outperforms the best SLMJs and GPT-4o-mini on overall performance across 11
out-of-distribution benchmarks, spanning absolute scoring, classification, and
pairwise preference tasks. It is the highest-scoring 8B generative model on
RewardBench, surpassing strong baselines like GPT-4o and specialized judges. To
achieve this, we develop a principled data curation strategy that augments
public datasets with synthetically generated critiques and ensures high quality
through filtering and dataset ablations. We train our model on a combined
direct preference optimization (DPO) and supervised fine-tuning (SFT) loss, and
produce a highly promptable evaluator that excels in real-world scenarios.
Selene Mini shows dramatically improved zero-shot agreement with human expert
evaluations on financial and medical industry datasets. It is also robust to
variations in prompt format. Preliminary results indicate that Selene Mini is
the top-ranking evaluator in a live, community-driven Judge Arena. We release
the model weights on HuggingFace
(https://hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B) and Ollama to encourage
widespread community adoption.