E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models
Recent reinforcement learning has enhanced the flow matching models on human preference alignment. While stochastic sampling enables the exploration of denoising directions, existing methods which optimize over multiple denoising steps suffer from sparse and ambiguous reward signals. We observe that the high entropy steps enable more efficient and effective exploration while the low entropy steps result in undistinguished roll-outs. To this end, we propose E-GRPO, an entropy aware Group Relative Policy Optimization to increase the entropy of SDE sampling steps. Since the integration of stochastic differential equations suffer from ambiguous reward signals due to stochasticity from multiple steps, we specifically merge consecutive low entropy steps to formulate one high entropy step for SDE sampling, while applying ODE sampling on other steps. Building upon this, we introduce multi-step group normalized advantage, which computes group-relative advantages within samples sharing the same consolidated SDE denoising step. Experimental results on different reward settings have demonstrated the effectiveness of our methods.
695b259f832867f253525d7b
Shengjun Zhang
claimed_verified
2026-01-08T08:33:36.350Z
65fd0dfdfdc5e8ee7d15f793
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Shengjun Zhang
zhangsj0722
user
zhangsj0722
695b259f832867f253525d7c
Zhang Zhang
695b259f832867f253525d7d
Chensheng Dai
695b259f832867f253525d7e
Yueqi Duan
E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models
Recent reinforcement learning has enhanced the flow matching models on human preference alignment. While stochastic sampling enables the exploration of denoising directions, existing methods which optimize over multiple denoising steps suffer from sparse and ambiguous reward signals. We observe that the high entropy steps enable more efficient and effective exploration while the low entropy steps result in undistinguished roll-outs. To this end, we propose E-GRPO, an entropy aware Group Relative Policy Optimization to increase the entropy of SDE sampling steps. Since the integration of stochastic differential equations suffer from ambiguous reward signals due to stochasticity from multiple steps, we specifically merge consecutive low entropy steps to formulate one high entropy step for SDE sampling, while applying ODE sampling on other steps. Building upon this, we introduce multi-step group normalized advantage, which computes group-relative advantages within samples sharing the same consolidated SDE denoising step. Experimental results on different reward settings have demonstrated the effectiveness of our methods.
Entropy-aware policy optimization method for reinforcement learning in flow matching models that improves exploration through SDE and ODE sampling strategies.
flow matching models reinforcement learning stochastic sampling denoising directions reward signals entropy awareness Group Relative Policy Optimization stochastic differential equations ordinary differential equations SDE sampling ODE sampling multi-step group normalized advantage