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Looped Language Models (LoopLMs) offer a parameter efficient approach to scaling reasoning by reusing shared parameters across recurrent computation steps. Despite their promise, effective post-training of LoopLMs remains challenging. Existing approaches either provide reward based supervision that is sparse or costly...
Counterfactual Trace On-Policy Distillation (CT-OPD), which combines completed teacher responses with trajectory masks from the current student, consistently enhances multimodal understanding and reasoning capabilities, and improves both visual understanding and image generation.
Results suggest that AAPT performs best when candidate actions can be enumerated in advance, whereas reactive execution remains stronger when they cannot, and on an external benchmark, AAPT matches the overall performance of a reactive baseline, although the two methods exhibit complementary strengths.
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