Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
cs.AI, cs.CL, cs.LG
Submitted: 2026-04-05
Updated: 2026-09-25
Code: https://github.com/gepa-ai/gepa
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- CL-bench: A Benchmark for Context Learning
- Learning to Share: Selective Memory for Efficient Parallel Agentic Systems
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- More Agents Is All You Need
- FiNER: Financial Numeric Entity Recognition for XBRL Tagging
- MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
- FrontierCS: Evolving Challenges for Evolving Intelligence
- An Empirical Model of Large-Batch Training
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
- Scaling Large Language Model-based Multi-Agent Collaboration
- Don't Decay the Learning Rate, Increase the Batch Size
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
- AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
- FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Mixture-of-Agents Enhances Large Language Model Capabilities
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