Ant Group Releases LLaDA2.2 Model with Levenshtein Edit and Reinforcement Learning for Agent Error Correction
Ant Group released the LLaDA2.2 model on July 27, incorporating Levenshtein edit and environmental feedback reinforcement learning. The model enables diffusion language models to correct errors while acting in real agent scenarios for the first time. This capability allows dynamic error correction during task execution, representing a significant advancement for diffusion language models in interactive agent tasks.
Ant released the LLaDA2.2 model on July 27. By introducing Levenshtein edit and environmental feedback reinforcement learning, LLaDA2.2 endows diffusion language models with the ability to 'correct errors while acting' in real agent scenarios.
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