Verified Self-Improvement
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Related Articles from SNS
Reliable Self-Improvement Training by Verifying Reasoning, Not Just Answers
arXiv:2603.21558v2 Announce Type: replace Abstract: Self-improvement training, where models learn from self-generated solutions, promises sustained capability gains but suffers from a pervasive failure mode: across multiple rounds, compounding reasoning errors cause accuracy to stall or degrade. We trace this drift to standard filtering criteria that retain solutions based solely on final answer correctness, which lets lucky guesses (correct answers with flawed reasoning) contaminate the...
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
arXiv:2604.01985v2 Announce Type: replace Abstract: General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learning which primarily focuses on optimal actions, a world model needs to be reliable over a vast space of suboptimal actions, which are often underrepresented in action-labeled robot interactions. To address this challenge, we propose World Action Verifier (WAV), a...
A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself. In contrast to the empirical success of self-improvement, the theoretical foundation of this generative, iterative procedure in a practical, finite-sample setting remains limited. We make progress toward this goal by modeling each round of self-improvement as maximum-likelihood fine-tuning on a reward-filtered distribution...
Self-Trained Verification for Training- and Test-Time Self-Improvement
arXiv:2605.30290v2 Announce Type: replace Abstract: Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; and at training time, through self-training methods. Both are gated by the same bottleneck: the verifier. V-R loops stall when verifier scores inflate while accuracy stagnates, and when feedback is too generic to act on; self-training fails similarly when bad...
Show HN: Komi-learn – continuous memory and self-improvement for coding agents
Continuous memory and self-improvement for coding agents. It learns how you work and recalls it automatically, with no commands. Works with Claude Code and Codex.
Sakana AI's Recursive Self-Improvement (RSI) Lab
The Next Paradigm of Artificial Intelligence As the world enters the era of artificial intelligence, Japan has a unique opportunity to reclaim its position at the frontier of global innovation. However, to achieve global leadership in AI and scientific discovery, we cannot simply stick to the conventional approach of brute-forcing monolithic models. We must leapfrog the current paradigm.
When AI Builds Itself: Our progress toward recursive self-improvement
For most of AI’s history, humans drove every step in its development cycle. But at Anthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work. Taken far enough, and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor.
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Announce Type: replace Abstract: Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code. Existing LLM-based approaches typically rely on brittle prompting or costly retraining, both of which offer limited generalization. Recent work suggests that large models can improve via experience reuse, but how to...
From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws
Announce Type: new Abstract: LLM-based agents increasingly rely on harnesses that provide execution environments, tool interfaces, context, lifecycle orchestration, observability, verification, and governance. Existing self-improving agents and automatic harness evolution methods mainly improve agents through runtime supervision, prompt optimization, workflow search, or harness modification based on final outcomes. However, they often fail to diagnose where the responsible evidence lies in...
Anthropic says AI labs need coordinated plan to halt development if risks rise
Anthropic says AI labs need coordinated plan to halt development if risks rise June 4 : Anthropic said on Thursday frontier AI developers should establish a coordinated, verifiable way to slow down or temporarily pause development if advanced systems begin improving themselves faster than society can manage the risks. AI that can build itself would be a major development in the history of technology, but "full recursive self-improvement also might increase the risks of humans losing control...