Agentic RL Infra Survey: 11 Chinese Labs' Approaches to Long-Horizon Credit Assignment

This article surveys Agentic RL infrastructure across 11 leading Chinese AI labs, analyzing their architectural choices (colocated vs. disaggregated), async/partial rollout strategies, staleness control, training-inference mismatch solutions, policy optimization algorithms (GRPO, GSPO, CISPO, etc.), sparse reward credit assignment, reward hacking defenses, and environment/sandbox management, revealing six emerging paradigms and unresolved debates.

Agentic RLGRPO VariantsLong-Horizon Credit Assignment

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Agentic RL Infra Survey: 11 Chinese Labs' Approaches to Long-Horizon Credit Assignment

Baobao Algorithm Notes

Feb 24, 2026 · Artificial Intelligence

The Bitter Lesson of Building Agentic RL in Terminal Environments

This article recounts the challenges of moving from single‑step RL with verifiable rewards to multi‑step agentic reinforcement learning in terminal environments, detailing infrastructure design, asynchronous pipelines, data quality checks, masking strategies, curriculum training, chunk‑based optimization, and practical lessons learned from large‑scale experiments.

Agentic RLAsynchronous TrainingEnvironment Augmentation

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The Bitter Lesson of Building Agentic RL in Terminal Environments

RL Infrastructure

Agentic RL Infra Survey: 11 Chinese Labs' Approaches to Long-Horizon Credit Assignment

The Bitter Lesson of Building Agentic RL in Terminal Environments