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Multi-Reward Reinforcement Learning for LLM Agents: Comparing PPO, GRPO, DAPO, and GDPO
New empirical follow-up: Part 2 compares seven trainer configurations on Qwen3-14B and our DEX gym , with no-think and thinking holdouts, interactive reward curves, and downloadable data. It is a separate experiment from the 27B table below and does not establish a universal trainer ranking.
If you only ever train language models on toy math puzzles, reinforcement learning feels simple: did the model output 42? If yes, reward is 1. If no, reward is 0.
The moment you try to train an aut...
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