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 autonomous agent for real-world enterprise work, however, a single scalar reward is an absolute illusion. In production, your agent has to juggle multiple competing, messy, non-commensurate priorities at the same time: The Main Mission (R₁): Did it actually solve the customer’s request? (e.g., execute the right SQL query, compile the circuit, return the right data payload). Execution Efficiency (R₂): Did it solve it elegantly in 3 tool calls, or did it run a wild 40-step loop that burned $4 in