---
title: README
emoji: 🐠
colorFrom: purple
colorTo: purple
sdk: static
pinned: true
license: apache-2.0
---

<div align="center">

# Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

[![arXiv](https://img.shields.io/badge/arXiv-2605.00754-b31b1b.svg)](https://arxiv.org/abs/2605.00754)
[![Models](https://img.shields.io/badge/%F0%9F%A4%97%20Models-Themis--RM-yellow)](https://huggingface.co/collections/project-themis/themis-reward-model-collection)
[![Datasets & Benchmarks](https://img.shields.io/badge/%F0%9F%A4%97%20Datasets%20%26%20Benchmarks-Themis-blue)](https://huggingface.co/collections/project-themis/themis-preference-datasets-and-benchmarks)
[![GitHub](https://img.shields.io/badge/GitHub-Themis-181717?logo=github)](https://github.com/iNeil77/Themis)
[![Docker](https://img.shields.io/badge/Docker-ineil77%2Fthemis-2496ED?logo=docker)](https://hub.docker.com/repository/docker/ineil77/themis/general)

</div>

> **Abstract:**
>
> 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 we profile 50+ code, math, and general-purpose RMs. Observing the limited proficiency of current RMs beyond scoring for functional correctness, we develop Themis-CodePreference, the largest open-source collection of code preferences to date (more than 350k preference pairs), and use it to train Themis-RM, a suite of multilingual code reward models for flexible multi-criteria scoring, ranging in size from 600M to 32B parameters. Our experiments and ablations demonstrate positive scaling trends, strong cross-lingual transfer when training on diverse preferences, and the importance of multi-criteria training for reliable code reward modeling.
>

Themis reward models are trained using the Bradley-Terry preference framework with a multi-stage data pipeline that mines, filters, scores, and assembles high-quality code preference pairs from open-source repositories. The models are evaluated on Code RewardBench (CRB), a benchmark of 8,866 preference pairs spanning 5 quality aspects and 8 programming languages.

## Pipeline Overview

The end-to-end pipeline has three phases: dataset construction, model training, and evaluation.

```
                          DATASET CONSTRUCTION
                          ────────────────────
  BigQuery (github_repos)
      │
      ▼
  ┌─────────────────────┐   ┌───────────────────┐   ┌──────────────────┐
  │ 1. Commit Mining    │──▶│ 2. Repo Filtering │──▶│ 3. Ext Filtering │
  │    (SQL)            │   │    (allowlists)   │   │    (lang → ext)  │
  └─────────────────────┘   └───────────────────┘   └──────────────────┘
                                                            │
      ┌─────────────────────────────────────────────────────┘
      ▼
  ┌──────────────────────┐   ┌──────────────────┐   ┌──────────────────┐
  │ 4. Content Retrieval │──▶│ 5. Deduplication │──▶│ 6. Aspect Filter │
  │    (git fetch)       │   │    (MinHash LSH) │   │    (ModernBERT)  │
  └──────────────────────┘   └──────────────────┘   └──────────────────┘
                                                            │
      ┌─────────────────────────────────────────────────────┘
      ▼
  ┌──────────────────────┐   ┌──────────────────┐   ┌──────────────────┐
  │ 7. LLM Scoring &     │-─▶│ 8. LLM-as-a-Judge│──▶│ 9. Training Data │
  │    Instruction Synth │   │    (A/B voting)  │   │    Assembly      │
  └──────────────────────┘   └──────────────────┘   └──────────────────┘
                                                            │
                          MODEL TRAINING                    │
                          ──────────────                    │
      ┌─────────────────────────────────────────────────────┘
      ▼
  ┌───────────────────────────────────────────────────────────────────┐
  │ Bradley-Terry preference training with FSDP2 on multi-node GPUs   │
  │ (BT loss + LM regularisation + magnitude penalty, Liger kernels)  │
  └───────────────────────────────────┬───────────────────────────────┘
                                      │
                          EVALUATION  │
                          ──────────  │
      ┌───────────────────────────────┘
      ▼
  ┌───────────────────────────────────────────────────────────────────┐
  │ Code RewardBench: 8,866 pairs × 5 aspects × 8 languages           │
  │ Evaluated across scalar, MoE, and generative RM architectures     │
  └───────────────────────────────────────────────────────────────────┘
```

## Results

Themis-RM models achieve best-in-class accuracy on [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench), a code-specific reward model benchmark, while also matching or exceeding much larger models on established general-domain benchmarks (RewardBench V1, RewardBench V2, JudgeBench). Models are grouped by parameter class; **bold** marks the best in each group.

| Model | [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench) | [RewardBench V1](https://huggingface.co/datasets/allenai/reward-bench) | [RewardBench V2](https://huggingface.co/datasets/allenai/reward-bench-v2) | [JudgeBench](https://huggingface.co/datasets/ScalerLab/JudgeBench) |
|---|---|---|---|---|
| | | | | |
| **32B - 72B Class** | | | | |
| [WorldPM-72B](https://huggingface.co/Qwen/WorldPM-72B-RLHFLow) | 76.96 | 90.88 | 67.92 | 55.21 |
| [Athene-RM-70B](https://huggingface.co/Nexusflow/Athene-RM-70B) | 78.39 | 91.22 | 68.76 | 63.45 |
| [Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.3-Nemotron-70B-Reward) | 81.19 | 93.88 | 70.49 | **73.47** |
| **[Themis-RM-32B](https://huggingface.co/project-themis/Themis-RM-32B)** | **91.82** | **94.89** | **72.34** | 71.65 |
| [AceCodeRM-32B](https://huggingface.co/TIGER-Lab/AceCodeRM-32B) | 62.95 | 23.58 | 67.98 | 66.77 |
| | | | | |
| **7B – 14B Class** | | | | |
| **[Themis-RM-14B](https://huggingface.co/project-themis/Themis-RM-14B)** | **91.19** | 94.11 | 71.44 | **70.85** |
| **[Themis-RM-8B](https://huggingface.co/project-themis/Themis-RM-8B)** | 89.78 | 93.69 | 65.87 | 69.97 |
| [Athene-RM-8B](https://huggingface.co/Nexusflow/Athene-RM-8B) | 76.58 | 87.48 | 62.96 | 61.12 |
| [CodeScaler-8B](https://huggingface.co/LARK-Lab/CodeScaler-8B) | 79.12 | 94.66 | 76.51 | 70.05 |
| [Skywork-Reward-V2-8B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-8B) | 79.97 | **94.76** | **76.93** | 67.90 |
| [AceCodeRM-7B](https://huggingface.co/TIGER-Lab/AceCodeRM-7B) | 71.11 | 22.74 | 63.16 | 61.09 |
| | | | | |
| **0.6B - 4B Class** | | | | |
| **[Themis-RM-4B](https://huggingface.co/project-themis/Themis-RM-4B)** | **88.39** | 92.46 | 63.81 | 68.02 |
| [CodeScaler-4B](https://huggingface.co/LARK-Lab/CodeScaler-4B) | 77.97 | **94.32** | **75.13** | **68.44** |
| [Skywork-Reward-V2-4B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-4B) | 79.27 | 94.06 | 74.26 | 65.43 |
| **[Themis-RM-1.7B](https://huggingface.co/project-themis/Themis-RM-1.7B)** | 83.04 | 89.17 | 56.22 | 63.29 |
| [CodeScaler-1.7B](https://huggingface.co/LARK-Lab/CodeScaler-1.7B) | 73.75 | 91.13 | 68.44 | 66.17 |
| [Skywork-Reward-V2-1.7B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-1.7B) | 75.60 | 91.64 | 67.71 | 66.48 |
| **[Themis-RM-0.6B](https://huggingface.co/project-themis/Themis-RM-0.6B)** | 79.26 | 83.41 | 49.61 | 63.84 |
| [Skywork-Reward-V2-0.6B](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-0.6B) | 72.77 | 86.32 | 60.83 | 63.65 |

## Datasets

All datasets are available on HuggingFace:

| Dataset | Description | Samples |
|---|---|---|
| [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench) | Code RM evaluation benchmark: 5 quality dimensions, 8 languages, 19 source subsets | 8,866 |
| [Themis-CodePreference](https://huggingface.co/datasets/project-themis/Themis-CodePreference) | Training data for the PM stage: code preferences across 5 criteria and 8 languages | 354,010 |
| [Themis-GeneralPreference](https://huggingface.co/datasets/project-themis/Themis-GeneralPreference) | Training data for the PT stage: general-domain and code retrieval preferences | 110,598 |
| [Themis-Git-Commits-Merged](https://huggingface.co/datasets/project-themis/git-commits-merged) | Single-file commits from merged PRs across 24 languages (intermediate, pre-classification) | ~8M |
| [Themis-Git-Commits](https://huggingface.co/datasets/project-themis/git-commits) | Raw mined single-file commits from permissively licensed repos (full unfiltered pool) | ~28M |

## Related Work

**[Distributed Training Tutorial](https://github.com/iNeil77/AWS_DistTraining_Tutorial)** — A companion tutorial by us that walks through multi-node distributed training of scalar reward models on cloud GPU clusters. Covers cluster provisioning, high-speed networking, container management, and FSDP-based training. Useful as a standalone guide for anyone looking to reproduce the Themis training setup or adapt it to their own reward modelling workloads. Follows a simplified recipe that leverages the Axolotl framework for training reward models with the Bradley-Terry loss.

## Citation

```bibtex
@article{themis2025,
  title={Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring},
  author={Paul, Indraneil and Gurevych, Iryna and Glava\v{s}, Goran},
  journal={arXiv preprint arXiv:2605.00754},
  year={2025}
}
```