seq2seq

This paper introduces Complementary Action Modeling (CAM), a task that identifies or generates procedural counterparts of automotive maintenance instructions by modifying the action phrase while preserving context. Using a German automotive dataset, the authors examine candidate matching and controlled Seq2Seq generation to model these complementary instructions.

MathFormer is a small seq2seq model that achieves ~98.6% accuracy on symbolic math tasks, suggesting that mathematical reasoning in LLMs may be large-scale structured pattern completion rather than true reasoning.

This paper applies Group Relative Policy Optimization (GRPO) to encoder-decoder Seq2Seq models for machine translation fine-tuning, using reference-free rewards (LaBSE and COMET-Kiwi) that require no parallel data, and achieves consistent improvements across 13 languages.