EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
cs.AI, cs.CL
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/harbor-framework/harbor
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- A General Language Assistant as a Laboratory for Alignment
- Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities
- FireAct: Toward Language Agent Fine-tuning
- HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems
- HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
- Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
- From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
- Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost
- Agents in the Large: Perception-Centered Architecture for Persistent Agents
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- Recursive Harness Self-Improvement
- Meta-Harness: End-to-End Optimization of Model Harnesses
- Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
- Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
- Code as Agent Harness
- AlphaEvolve: A coding agent for scientific and algorithmic discovery
- gpt-oss-120b & gpt-oss-20b Model Card
- Training Software Engineering Agents and Verifiers with SWE-Gym
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