HAEPO weights each trajectory by its softmax-normalized cumulative log-likelihood, adds entropy and KL penalties, and matches or slightly surpasses PPO, GRPO, and DPO on small RL and summarization tasks.
Exploration in deep reinforcement learning: From single-agent to multiagent domain
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HAEPO: History-Aggregated Exploratory Policy Optimization
HAEPO weights each trajectory by its softmax-normalized cumulative log-likelihood, adds entropy and KL penalties, and matches or slightly surpasses PPO, GRPO, and DPO on small RL and summarization tasks.