Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring
Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling. Research on the application of RMs in code generation, however, has been comparatively sparse, with existing work largely focusing on execution feedback. This choice constrains post-training to optimizing functional correctness over self-contained executable code. In this work, we examine the training and evaluation of multilingual, multi-criteria code RMs. To this end, we first compile Themis-CodeRewardBench, a benchmark to evaluate code RMs across five preference dimensions (i.e., criteria) and eight programming languages, on which ...
| Authors | Indraneil Paul, Goran Glavaš, Iryna Gurevych |
|---|---|
| Primary category | cs.SE |
| Published | 1 May 2026 |
| Last updated at source | 9 August 2026 |
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