mlsanityData to 7 Oct 2026

Self-Improvement in Language Models: The Sharpening Mechanism

arXiv 2412.01951 ↗First posted 2 Dec 2024cs.AI cs.CL cs.LGcited by 102

On the map: Synthetic data and self-improvement for LLMs → Self-rewarding alignment and self-training

Abstract (arXiv)

Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance without external feedback. It is impossible for this self-improvement to create information that is not already in the model, so why should we expect that this will lead to improved capabilities? We offer a new perspective on the capabilities of self-improvement through a lens we refer to as sharpening. Motivated by the observation that language models are often better at verifying response quality than they are at generating correct responses, we formalize self-improvement as using the model itself as a verifier during post-training in order to ``sharpen'' the model to one placing large mass on high-quality sequences, thereby amortizing the expensive inference-time computation of generating good sequences. We begin by introducing a new statistical framework for sharpening in which the learner aims to sharpen a pre-trained base policy via sample access, and establish fundamental limits. Then we analyze two natural families of self-improvement algorithms based on SFT and RLHF. We find that (i) the SFT-based approach is minimax optimal whenever the initial model has sufficient coverage, but (ii) the RLHF-based approach can improve over SFT-based self-improvement by leveraging online exploration, bypassing the need for coverage. Finally, we empirically validate the sharpening mechanism via inference-time and amortization experiments. We view these findings as a starting point toward a foundational understanding that can guide the design and evaluation of self-improvement algorithms.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Placed in Self-rewarding alignment and self-training, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 68 of its 105 references that are in the map. First posted on arXiv 2 Dec 2024.

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68 of 105 references.

Closest ancestors · 7

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  1. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug
  2. 2024Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge28 Jul
  3. 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improve25 Jul
  4. 2024Self-Play Preference Optimization for Language Model Alignment1 May
  5. 2024Self-Rewarding Language Models18 Jan
  6. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models2 Jan
  7. 2023Language Model Self-improvement by Reinforcement Learning Contemplation23 May

Other references · 61

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Citing papers per year: 2024: 2; 2025: 47; 2026: 53.

102 papers.

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