Blog•Mar 13, 2026
Your LLM Is Ignoring Its Own Mistakes (And Three Papers That Show How to Fix It)
LLMs excel at generating first‑pass outputs but struggle to learn from real‑time feedback. Recent research—Meta’s RLEF, Anthropic’s Constitutional AI, and the ReAct framework—demonstrates that reinforcement learning, self‑generated critique, and explicit reasoning traces dramatically improve error correction and tool use. Across code generation, safety tuning, and interactive tasks, these methods outperform traditional fine‑tuning and prompting. The consensus is that robust feedback loops, not larger models, are the key to reliable AI agents.
