This article surveys five years of large‑model reinforcement learning, detailing the evolution from PPO + RLHF to DPO and GRPO, comparing reward‑model‑based and verifiable‑reward approaches, discussing multi‑agent extensions, and evaluating open‑source frameworks for training LLM‑driven agents.
This article reviews the five‑year evolution of reinforcement‑learning techniques for large language models, comparing PPO, DPO, GRPO and emerging multi‑agent approaches, analyzing their reward signals, practical trade‑offs, and the open‑source frameworks that support them.
The article reviews five years of LLM‑centric reinforcement learning, tracing the evolution from early Q‑learning to PPO, then to Direct Preference Optimization, Group Relative Policy Optimization, and finally multi‑agent RL, detailing each method’s mechanics, strengths, failure modes, practical considerations, and emerging open‑source toolchains.
The IJCAI‑2022 Neural MMO Massive AI Team Survival Challenge demonstrated that deep reinforcement‑learning agents can achieve sophisticated cooperation and competition among 128 agents in a large‑scale MMO‑style world, highlighting the growing focus on decision‑AI, the effectiveness of self‑play and CTDE, and the platform’s potential for future research into population‑level behavior, economics, and complex real‑world decision making.
