Learning to Self-Evolve
arXiv:2603.18620v1 Announce Type: new Abstract: We introduce Learning to Self-Evolve (LSE), a reinforcement learning framework that trains large language models (LLMs) to improve their own contexts at test time. We situate LSE in the setting of test-time self-evolution, where a model iteratively refines its context from feedback on seen problems to perform better on new ones. Existing approaches rely entirely on the inherent reasoning ability of the model and never explicitly train it for this task. LSE reduces the multi-step evolution problem to a single-step RL objective, where each context edit is rewarded by the improvement in downstream performance. We pair this objective with a tree-guided evolution loop. On Text-to-SQL generation (BIRD) and general question answering (MMLU-Redux), a 4B-parameter model trained with LSE outperforms self-evolving policies powered by GPT-5 and Claude Sonnet 4.5, as well as prompt optimization methods including GEPA and TextGrad, and transfers to gu
arXiv:2603.18620v1 Announce Type: new Abstract: We introduce Learning to Self-Evolve (LSE), a reinforcement learning framework that trains large language models (LLMs) to improve their own contexts at test time. We situate LSE in the setting of test-time self-evolution, where a model iteratively refines its context from feedback on seen problems to perform better on new ones. Existing approaches rely entirely on the inherent reasoning ability of the model and never explicitly train it for this task. LSE reduces the multi-step evolution problem to a single-step RL objective, where each context edit is rewarded by the improvement in downstream performance. We pair this objective with a tree-guided evolution loop. On Text-to-SQL generation (BIRD) and general question answering (MMLU-Redux), a 4B-parameter model trained with LSE outperforms self-evolving policies powered by GPT-5 and Claude Sonnet 4.5, as well as prompt optimization methods including GEPA and TextGrad, and transfers to guide other models without additional training. Our results highlight the effectiveness of treating self-evolution as a learnable skill.
Executive Summary
The article introduces Learning to Self-Evolve (LSE), a reinforcement learning framework that enables large language models (LLMs) to improve their own contexts at test time. LSE employs a tree-guided evolution loop and rewards each context edit based on downstream performance. Experiments on Text-to-SQL generation and general question answering demonstrate the effectiveness of LSE in outperforming existing self-evolving policies and prompt optimization methods. The results indicate that treating self-evolution as a learnable skill is a promising approach. This innovation has significant implications for the development of more efficient and effective language models.
Key Points
- ▸ LSE reduces the multi-step evolution problem to a single-step RL objective.
- ▸ A 4B-parameter model trained with LSE outperforms existing self-evolving policies and prompt optimization methods.
- ▸ LSE employs a tree-guided evolution loop to guide the refinement of the model's context.
Merits
Strength of LSE
LSE's ability to reduce the multi-step evolution problem to a single-step RL objective is a significant strength, enabling more efficient and effective self-evolution.
Effectiveness of LSE
The experimental results demonstrate the effectiveness of LSE in outperforming existing self-evolving policies and prompt optimization methods.
Transferability of LSE
LSE's ability to transfer to guide other models without additional training is a notable advantage.
Demerits
Limitation of LSE
The article does not thoroughly address the potential challenges and limitations of LSE, such as the potential for overfitting or the need for extensive computational resources.
Expert Commentary
The introduction of LSE is a significant advancement in the field of natural language processing, offering a new perspective on the development of self-evolving language models. The ability of LSE to reduce the multi-step evolution problem to a single-step RL objective is a notable innovation, enabling more efficient and effective self-evolution. However, further research is needed to fully address the potential challenges and limitations of LSE. The implications of LSE are far-reaching, with potential applications in a range of fields, including natural language processing, machine learning, and AI development.
Recommendations
- ✓ Future research should focus on addressing the potential challenges and limitations of LSE, such as overfitting and the need for extensive computational resources.
- ✓ The development of LSE has significant practical implications, and researchers should explore its potential applications in various fields, including natural language processing and machine learning.
