FlavourBench: Executable Culinary Reward Maps for Language Model Evaluation and Post-Training
cs.AI, cs.CY, cs.LG, cs.SE
Submitted: 2026-08-20
Updated: 2026-09-02
Comments: 18 pages, 11 figures. Evaluation of 27 frontier language-model endpoints on 534 identical tasks per model, comprising 14,418 scored model-task cells. Adds reward-map sensitivity, selection and metric robustness, held-out Recipe1MSubs substitution validation, and a preregistered controlled reward-transfer study. Code, dataset, and interactive leaderboard links remain unchanged
License: http://creativecommons.org/licenses/by/4.0/
The gist: We introduce FlavorBench: a benchmark for Compiling Dense Deterministic Answer Maps from a Versioned Culinary Embeddings Model.
Terminology
Abstract
We introduce FlavorBench: a benchmark for Compiling Dense Deterministic Answer Maps from a Versioned Culinary Embeddings Model. We test 27 frontier large language model endpoints on 534 substitution, pairing and constraining tasks for tasks that request a 3-ingredient portfolio from 8 candidates and score all 56 resulting portfolios. We conducted multiplicity-controlled paired tests on 101 of 351 model contrasts for this task-set. The largest point estimate on this task-set was achieved by Grok 4.6 at 65.1. The same rankings for this task-set were also achieved on several independently-compiled panels (using a variety of familiar metrics, task filters, etc.) and 3 public Epicure checkpoints. We present a 3-seed post-training study where LoRA SFT of a Qwen3-0.6B checkpoint on 270 optimal answers for Epicure to score on this task-set resulted in a 13.3 point gain on 84 anchor-disjoint maps (compared to format and label-matched control; 95% CI: 6.52, 20.29; p = 0.000170).
Sources
- Learning to Substitute Ingredients in Recipes
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- RewardBench: Evaluating Reward Models for Language Modeling
- Qwen3 Technical Report
- Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings
- Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings
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