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cs.AI2026
Learning to Reason Efficiently with A* Post-Training
Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3
Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language infer…
cs.AI2026
How Deep Can LLMs Learn to Reason? Expressiveness Is Key
Tianle Wang, Zhaoyang Wang, Guangchen Lan +4
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered…