SPARK: Synergistic Policy And Reward Co-Evolving Framework
Authors: name: Ziyu Liu, name: Yuhang Zang, name: Shengyuan Ding, name: Yuhang Cao, name: Xiaoyi Dong, name: Haodong Duan, name: Dahua Lin, name: Jiaqi Wang
Organization: Intern Large Models

Recent Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
increasingly use Reinforcement Learning (RL) for post-pretraining, such as RL
with Verifiable Rewards (RLVR) for objective tasks and RL from Human Feedback
(RLHF) for subjective tasks. However, RLHF incurs high costs and potential
reward-policy mismatch due to reliance on human preferences, while RLVR still
wastes supervision by discarding rollouts and correctness signals after each
update. To address these challenges, we introduce the Synergistic Policy And
Reward Co-Evolving Framework (SPARK), an efficient, on-policy, and stable
method that builds on RLVR. Instead of discarding rollouts and correctness
data, SPARK recycles this valuable information to simultaneously train the
model itself as a generative reward model. This auxiliary training uses a mix
of objectives, such as pointwise reward score, pairwise comparison, and
evaluation conditioned on further-reflection responses, to teach the model to
evaluate and improve its own responses. Our process eliminates the need for a
separate reward model and costly human preference data. SPARK creates a
positive co-evolving feedback loop: improved reward accuracy yields better
policy gradients, which in turn produce higher-quality rollouts that further
refine the reward model. Our unified framework supports test-time scaling via
self-reflection without external reward models and their associated costs. We
show that SPARK achieves significant performance gains on multiple LLM and LVLM
models and multiple reasoning, reward models, and general benchmarks. For
example, SPARK-VL-7B achieves an average 9.7% gain on 7 reasoning benchmarks,
12.1% on 2 reward benchmarks, and 1.5% on 8 general benchmarks over the
baselines, demonstrating robustness and broad generalization.