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Experiments on WebShop and ALFWorld with different backbones show that TRIAL outperforms GRPO across all eight combinations of backbone, environment, and evaluation metric, while achieving the best or tied-best performance among six methods on six of them.
Results show that self-contamination is a trainable component of the LiC gap, and propose MAIGO, an on-policy self-distillation method that reduces this contamination using history-cleaned references from the model's own policy.
A novel Dual Self-Consistency Reinforcement Learning optimization paradigm is introduced, which utilizes Round-Trip Verification to penalize degenerate code and boost overall self-consistency in TikZ code.
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