aiqbench analysis
Agentic Critical Training introduces a reinforcement learning approach using verifiable rewards to teach language models to distinguish expert actions from plausible errors, addressing a key limitation in current imitation learning methods. By training agents to judge actions directly without requiring reference rationales, this technique achieves significant performance gains on benchmarks like ScienceWorld and MATH-500 across various model sizes. The method demonstrates that action comparison complements generation capabilities, offering a robust warm-up strategy before standard fine-tuning or reinforcement learning pipelines.
Agentic Critical Training
arXiv:2603.08706v2 Announce Type: replace Abstract: 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 reference rationales, and allows pair reuse across model sizes. ACT is a warm-up before IL, optionally followed by RL; inference requires no candidate comparison. Across Qwen3-8B and Olmo-3-7B-Instruct on ALFWorld-ID, WebShop, and ScienceWorld, ACT yields average gains of 5.85 points over IL and 4.12 points over IL$\to$RL without ACT, while also improving ALFWorld-OOD. With Olmo on ScienceWorld, the full pipeline gains 15.36 points over CoT prompting and 9.23 points over IL$\to$RL without ACT. Both ACT$\to$IL and the full pipeline outperform supervised reflection baselines. Controls with fixed pairs or matched training durations show that the gains stem from the ACT objective rather than additional data or training. Without reasoning-specific post-training, the standalone ACT checkpoint achieves the highest mean among evaluated models on MATH-500 and GPQA-Diamond, showing that action comparison complements generation.
Abstract: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 reference rationales, and allows pair reuse across model sizes. ACT is a warm-up before IL, optionally followed by RL; inference requires no candidate comparison. Across Qwen3-8B and Olmo-3-7B-Instruct on ALFWorld-ID, WebShop, and ScienceWorld, ACT yields average gains of 5.85 points over IL and 4.12 points over IL$\to$RL without ACT, while also improving ALFWorld-OOD. With Olmo on ScienceWorld, the full pipeline gains 15.36 points over CoT prompting and 9.23 points over IL$\to$RL without ACT. Both ACT$\to$IL and the full pipeline outperform supervised reflection baselines. Controls with fixed pairs or matched training durations show that the gains stem from the ACT objective rather than additional data or training. Without reasoning-specific post-training, the standalone ACT checkpoint achieves the highest mean among evaluated models on MATH-500 and GPQA-Diamond, showing that action comparison complements generation.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2603.08706 [cs.AI]
(or arXiv:2603.08706v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.08706 Focus to learn more arXiv-issued DOI via DataCite
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