Language Models Learn to Improve Language Models

Humans continuously learn from experience, whereas conventional large language model (LLM) evaluations ignore the models' ability to improve through inference-time interaction. In this paper, we study how LLMs learn from iterative experience at test time, a setting we refer to as Chain-of-Experience (CoE), where models accumulate experiential traces through iterative interactions with self or environmental feedback to form a continual improvement loop beyond zero-shot inference. We instantiate CoE with diverse feedback mechanisms, including model self-feedback and environmental signals such as correctness or public coding test pass rates, and evaluate across math, coding, and knowledge domains using 8 LLMs, including GPT-5, Gemini-2.5 Pro, Claude-4.5 Sonnet. Our study shows that leveraging iterative experience consistently outperforms feedback-free baselines, achieving substantial gains with self feedback alone, alongside a 5.6% overall improvement and 19% lower API cost across tasks and models. We further show that combining complementary feedback channels (e.g., model and correctness signals) yields additional gains, and that CoE delivers higher accuracy per token than existing test-time strategies. We observe a positive correlation between LLM base ability and improvement capacity, and show that models remain robust under weak or spurious feedback, with different feedback contributing to distinct improvement aspects and most gains emerging early in the iterations.

Large language models (LLMs) can improve through reinforcement learning with verifiable rewards (RLVR), but many settings lack reference answers or external verifiers. Label-free self-improvement instead constructs training signals from the model's own outputs. Because the same policy generates both the candidate responses and the signals derived from them, each update also changes the candidates available for future training, creating a self-reinforcing feedback loop. We identify a failure mode of this loop, which we call selection-induced diversity collapse: repeated self-selection can progressively narrow the candidate pool and make alternative correct solutions harder to sample. To address this problem, we propose EVOL-RL, which balances selection and variation. Majority voting provides a stable selection signal, while embedding-based novelty refines credit among responses with the same selection status; entropy regularization and asymmetric clipping further support this process by maintaining diverse rollouts and preserving rare, high-advantage updates. Across multiple training distributions, model scales, backbones, and matched label-free baselines, EVOL-RL consistently improves pass@n while generally maintaining or improving pass@1. For example, when trained without labels on AIME24, Qwen3-4B-Base improves over TTRL on held-out AIME25 from 4.6% to 17.1% in pass@1 and from 18.5% to 42.0% in pass@16. Training analyses further show that when the current majority is wrong, EVOL-RL better preserves alternative correct solutions. The code is available at: https://github.com/YujunZhou/EVOL-RL.

As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach human-level capabilities in certain domains, human feedback may no longer provide sufficiently informative signals for further improvement. At the same time, the growing ability of models to make autonomous decisions and execute complex actions naturally enables abstractions in which components of the model development process can be progressively automated. Together, these challenges and opportunities have driven increasing interest in self-improvement, where models autonomously generate data, evaluate outputs, and iteratively refine their own capabilities. In this paper, we present a system-level perspective on self-improving language models and introduce a unified framework that organizes existing techniques. We conceptualize the self-improvement system as a closed-loop lifecycle, consisting of four tightly coupled processes: data acquisition, data selection, model optimization, and inference refinement, along with an autonomous evaluation layer throughout the process. Within this framework, the model itself plays a central role in driving each stage: collecting or generating data, selecting informative signals, updating its parameters, and refining outputs, while the autonomous evaluation layer continuously monitors progress and guides the improvement cycle across stages. Following this lifecycle perspective, we systematically review and analyze representative methods for each component from a technical standpoint. We further examine current limitations, potential risks, and prominent applications, and outline our vision for future research toward fully self-improving LLMs.