Long-horizon RL may favor PPO
A Zhihu post published on June 24, 2026, argues that long, noisy agentic rollouts may make credit assignment harder for GRPO and make PPO's value model useful.
Source: Por que RL de longo horizonte pode favorecer PPO em vez de GRPO (zhihu.com). Text prepared with AI from this source.
What happened and what to do
In a post published on Zhihu on June 24, 2026, the author argues that GRPO's sampled baseline suits short reinforcement-learning tasks with LLMs. In longer and noisier agentic rollouts, however, assigning credit to actions becomes harder, which may make PPO's value model useful. The discussion contrasts avoiding a critic with GRPO against value modeling for tasks with longer horizons.
A company training agents can turn this question into a controlled experiment: define short and long tasks, record trajectories, rewards, and quality metrics, and compare GRPO and PPO on performance, stability, and cost. An evaluation pipeline and dashboard can track results and inform the method choice, without assuming that either approach is always superior.
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