Agentic Critical Training
Imitation learning (IL) teaches language-model agents to reproduce expert actions but not to distinguish them from plausible mistakes. Self-reflection methods expose models to alternatives yet use supervised fine-tuning (SFT) to imitate fixed rationales and actions. We introduce Agentic Critical Training (ACT), which uses reinforcement learning with verifiable rewards (RLVR) to train models to judge actions directly. At each expert-trajectory state, ACT pairs an expert action with an alternative sampled from the initial policy and randomizes their order. The model generates its own reasoning but is rewarded only for selecting the expert action. ACT reuses demonstrations, requires no refer...
| Authors | Weize Liu, Minghui Liu, Sy-Tuyen Ho et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 March 2026 |
| Last updated at source | 8 October 2026 |
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