Hugging Face Trending Papers
CAFE: Self-Improving Search Agents Need Co-Evolving Feedback
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Aug 26
CAFE: Self-Improving Search Agents Need Co-Evolving Feedback
CAFE (Coupled Agent–Feedback Evolution) is a framework that lets a shared‑parameter model alternate between acting as a search agent and as a critic that provides corrective feedback. By learning when to request feedback and how to use it, CAFE trains the agent to recover from its own failures and shapes rewards both online and offline. Experiments on seven search benchmarks show that CAFE outperforms other RL‑based agents, maintains gains on out‑of‑domain tests, and reduces hallucinations, indicating that co‑evolving feedback is essential for self‑improving search agents.
arXiv AI
Jul 28
SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents
arXiv:2607. 23263v1 Announce Type: new Abstract: Deciding whether a trajectory actually fulfills its instruction governs how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning.
arXiv AI
Aug 11
Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards
arXiv:2608. 07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence.
Hugging Face Trending Papers
Sep 21
TTSE: A Two-Track Online Self-Evolution Framework
As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a core problem for achieving long-term autonomy. Curr...
arXiv AI
Aug 10
The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
arXiv:2608. 06714v1 Announce Type: new Abstract: Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods.
arXiv Machine Learning
Sep 22
TTSE: A Two-Track Online Self-Evolution Framework
arXiv:2609.24289v1 Announce Type: new Abstract: As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a co...