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<h1 class="title is-1 publication-title">Atla Selene Mini:<br>A General Purpose Evaluation Model</h1> |
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<b>Andrei Alexandru</b><sup>1</sup>,</span> |
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<b>Antonia Calvi</b><sup>1</sup>,</span> |
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<b>Henry Broomfield</b><sup>1</sup>,</span> |
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<b>Jackson Golden</b><sup>1</sup>,</span> |
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<span class="author-block"> |
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<b>Kyle Dai</b><sup>1</sup>,</span> |
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<b>Mathias Leys</b><sup>1</sup>,</span> |
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<b>Maurice Burger</b><sup>1</sup>,</span> |
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<b>Max Bartolo</b><sup>2,3</sup>,</span> |
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<b>Roman Engeler</b><sup>1</sup>,</span> |
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<b>Sashank Pisupati</b><sup>1</sup>,</span> |
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<b>Toby Drane</b><sup>1</sup>,</span> |
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<b>Young Sun Park</b><sup>1</sup></span> |
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<span class="author-block"><sup>1</sup>atla,</span> |
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<span class="author-block"><sup>2</sup>University College London,</span> |
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<span class="author-block"><sup>3</sup>Cohere</span> |
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<a href="https://hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B" target="_blank" |
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class="external-link button is-normal is-rounded is-dark"> |
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<span>HuggingFace</span> |
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<a href="https://ollama.com/atla/selene-mini" target="_blank" |
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<span>Ollama</span> |
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<h2 class="title is-3">Abstract</h2> |
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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. |
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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. |
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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 and Ollama to encourage widespread community adoption. |
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<img src="/api/placeholder/800/400" alt="Performance comparison"> |
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<b>Figure 1:</b> Atla Selene Mini outperforms current state-of-the-art SLMJs: a) Overall task-average performance, comparing Atla Selene Mini (black) with the best and most widely used SLMJs. b) Breakdown of performance by task type and benchmark. |
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<h2 class="title is-3">Methods</h2> |
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Selene Mini is optimized for fast inference, high performance, and promptability. It is a general-purpose evaluator, and is trained to respond with both critiques and judgments in order to deliver actionable insights. To achieve this, we fine-tuned a Llama 3.1 8B Instruct model on a curated mixture of 16 publicly available datasets, totaling 577k data points. |
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<img src="/api/placeholder/800/400" alt="Data curation strategy"> |
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<b>Figure 2:</b> Data curation strategy: The process of transforming a candidate dataset (left) into the final training mix (right). Yellow boxes indicate filtering steps, purple represents synthetic generation of chosen and rejected pairs for preference optimization. |
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<h2 class="title is-3">Results</h2> |
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<h3 class="title is-4">Benchmark Performance</h3> |
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We assess the performance of Selene Mini on 11 out-of-distribution benchmarks, spanning three different types of evaluation tasks: absolute scoring, classification, and pairwise preference. |
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<img src="/api/placeholder/800/400" alt="Real-world evaluation"> |
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<b>Figure 3:</b> Real-world evaluation: a) Performance on domain-specific industry benchmarks b) Performance on RewardBench with different prompt formats c) Performance measured by ELO scores in Judge Arena. |
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<h2 class="title is-3">Discussion</h2> |
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In this work, we introduce Atla Selene Mini, demonstrating that effective general-purpose evaluation can be achieved in smaller model architectures through principled data curation and a hybrid training objective (DPO + SFT). The model's strong performance across benchmarks, particularly on absolute scoring tasks – which represent the most common and useful form of evaluation in practice – suggests that careful attention to training data quality can be as impactful as increased model size for evaluation capabilities. |
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Looking ahead, we anticipate two emerging frontiers that will shape the future of AI evaluation. First is the rise of agent-based systems that combine language models with external tools and APIs, creating more powerful and versatile AI systems. Second is the increasing use of inference-time compute – systems that perform additional reasoning steps during inference to generate higher-quality outputs. |
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