Towards Foundation Decision Models: Drex
The paper presents Drex, a family of four open decision models (8B, 9B, 35B-A3B, 27B) that return a probability distribution over user-supplied options in a single forward pass without autoregressive decoding.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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The paper presents Drex, a family of four open decision models (8B, 9B, 35B-A3B, 27B) that return a probability distribution over user-supplied options in a single forward pass without autoregressive decoding. Training combines supervised fine-tuning with RLAF, a reinforcement learning method blending verified outcomes with a reasoning judge under a KL constraint. Ablations reportedly show RLAF improves accuracy and consistency losses reduce option-order flips from 6.3% to 4.2%. The authors report Drex Max scoring 67.52 on Decision Index 0.4.1 versus 60.70 for Jev 1.14.0, and state that weights and inference code are open.
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Drex returns a probability distribution over user-supplied options in a single forward pass, without autoregressive decoding.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseAblations reported in the text show consistency losses cut option-order flips from 6.3% to 4.2%.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseDrex Max scored 67.52 on Decision Index 0.4.1, versus 60.70 for Jev 1.14.0, per the authors.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe authors state all weights and inference code are open-sourced on GitHub.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Decision models that output calibrated distributions without generation could offer more consistent, lower-latency answers for production classification, judgment, and scoring tasks. Reported gains rest on the authors' own benchmarks and ablations.
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