InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward
  Model
Authors: name: Yuhang Zang, name: Xiaoyi Dong, name: Pan Zhang, name: Yuhang Cao, name: Ziyu Liu, name: Shengyuan Ding, name: Shenxi Wu, name: Yubo Ma, name: Haodong Duan, name: Wenwei Zhang, name: Kai Chen, name: Dahua Lin, name: Jiaqi Wang

Despite the promising performance of Large Vision Language Models (LVLMs) in
visual understanding, they occasionally generate incorrect outputs. While
reward models (RMs) with reinforcement learning or test-time scaling offer the
potential for improving generation quality, a critical gap remains: publicly
available multi-modal RMs for LVLMs are scarce, and the implementation details
of proprietary models are often unclear. We bridge this gap with
InternLM-XComposer2.5-Reward (IXC-2.5-Reward), a simple yet effective
multi-modal reward model that aligns LVLMs with human preferences. To ensure
the robustness and versatility of IXC-2.5-Reward, we set up a high-quality
multi-modal preference corpus spanning text, image, and video inputs across
diverse domains, such as instruction following, general understanding,
text-rich documents, mathematical reasoning, and video understanding.
IXC-2.5-Reward achieves excellent results on the latest multi-modal reward
model benchmark and shows competitive performance on text-only reward model
benchmarks. We further demonstrate three key applications of IXC-2.5-Reward:
(1) Providing a supervisory signal for RL training. We integrate IXC-2.5-Reward
with Proximal Policy Optimization (PPO) yields IXC-2.5-Chat, which shows
consistent improvements in instruction following and multi-modal open-ended
dialogue; (2) Selecting the best response from candidate responses for
test-time scaling; and (3) Filtering outlier or noisy samples from existing
image and video instruction tuning training data. To ensure reproducibility and
facilitate further research, we have open-sourced all model weights and
training recipes at https://github.com/InternLM/InternLM-XComposer