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Atla Selene Mini: A General Purpose Evaluation Model

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arxiv 2501.17195 v1 pith:YOK4N5EJ submitted 2025-01-27 cs.CL cs.AI

classification cs.CLcs.AI
keywords miniselenemodelevaluatoratladatasetspreferenceablations
verification ladder T0 review T1 audit T2 compute T3 formal
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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. 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.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Matched two-pass experiments show human revisers improve on objective and subjective tasks, while LLM self-revision yields near-zero information gain on objective tasks and negative information gain on subjective task...

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    Persistent SAEs learn per-feature persistence coefficients from reconstruction, splitting features into fast local detectors and slow topic-tracking states that retain prompt-injection signals over long contexts.

  3. Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization

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  4. ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments

    cs.CL 2025-05 conditional novelty 6.0 of 10

    ReliableEval estimates the minimum number of meaning-preserving prompt resamplings needed to make an LLM evaluation reliable, and applies it to show frontier LLMs are notably prompt-sensitive.

  5. Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation

    cs.LG 2026-02 conditional novelty 4.0 of 10

    A taxonomy-driven survey arguing that reward design is the central mechanism shaping reliable LLM reasoning, with maps of reward paradigms, reward-hacking failure modes, and benchmark pitfalls.

  6. Reward Reasoning Model

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    Reward models that think before judging, trained via reinforcement learning without human-written reasoning traces, outperform standard reward models and improve with more test-time compute.

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