Papers

Filtered to verifiable environments · clear filter

Browse by term

continual learning 86reinforcement learning 48benchmarking 13large language models 12benchmarks 11vision-language models 10language models 9robotics 7world models 7natural language processing 6recursive self-improvement 6generative models 5on-policy distillation 5video generation 5attention mechanisms 4coding agents 4LLMs 4multi-agent systems 4multimodal learning 4multimodal models 4self-distillation 4self-supervised learning 4transformers 4vision-language-action models 4world modeling 4agent-based systems 3agentic models 3agentic search 3agents 3autonomous systems 3

Matching papers

DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment

84 upvotes · 8 JUL 2026 · Xinyu Geng, Xuanhua He, Sixiang Chen et al.

This paper introduces a framework called DeepSearch-Evolve, which helps train self-improving web agents by iteratively refining their performance using their own experience. Practitioners might care because this approach can lead to more efficient and effective agents that can learn from their own mistakes.

web agentsself-distillationcontinual learningreinforcement learningverifiable environments