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cs.LG2026
Learning, Fast and Slow: Towards LLMs That Adapt Continually
Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal +6
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters forces them to absorb task-specific in…
cs.AI2026
Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks
Abhranil Chandra, Ayush Agrawal, Arian Hosseini +4
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capabl…