LLM Training Does Not Leverage Chain-of-Thought as Self-Supervision Signal

Large language models trained without explicit reasoning steps perform poorly on arithmetic and logical tasks, yet the same models improve significantly when allowed to reason before answering. The poster proposes that this gap represents an untapped training signal — using the model's own chain-of-thought outputs to penalize responses that contradict reasoned answers. This is fundamentally a research hypothesis rather than a validated pain point experienced by a defined user group.