This article details the second part of a multi-reward reinforcement learning benchmark, focusing on how different algorithms perform on unseen tasks. The study tested seven RL algorithms, including CISPO and DAPO, using the Qwen3-14B model in a decentralized exchange arbitrage gym. Results showed that while CISPO achieved a high training reward, its performance on frozen test tasks significantly degraded compared to the untrained baseline model, highlighting a potential 'generalization trap' where training metrics can be misleading. AI
IMPACT Highlights potential pitfalls in AI model training, where high training rewards may not translate to generalization on new problems.
RANK_REASON The item describes an empirical benchmark of reinforcement learning algorithms on unseen tasks, which constitutes research. [lever_c_demoted from research: ic=1 ai=1.0]
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