A Zhihu post published on June 24, 2026 compares GRPO and PPO for reinforcement learning tasks with LLMs, highlighting credit-assignment challenges in long, noisy agentic rollouts.
A post on Zhihu, published on June 24, 2026, argues that GRPO’s sampled baseline suits short reinforcement learning tasks with LLMs. It examines the tradeoff between avoiding a critic with GRPO and using value modeling with PPO.
For longer, noisier agentic rollouts, the post says, credit assignment becomes harder, which may make PPO’s value model useful. Consult the original Zhihu post and check its arguments and context there; the available summary gives no experiments or quantitative results.