Language Model Self-Improvement

The field studies how language models can improve their reasoning, instruction following, coding, and other skills by learning from their own attempts and interactions. Common approaches include self-training and self-distillation on generated responses, reinforcement learning and challenger–solver self-play, and test-time adaptation through memory, skills, or evolving prompts. Feedback ranges from exact checks such as code execution and formal proof to model-generated critiques, preferences, and confidence signals, with evaluation spanning math and coding benchmarks as well as agent and multimodal tasks. The central tension is whether these internal signals are reliable enough to produce transferable gains: noisy feedback, narrow curricula, and repeated selection can cause regressions or collapse, while oracle-supervised performance often remains difficult to match.