G"odel Forest: Balancing Search Depth and Breadth for Data-Centric Recursive Self-Improvement

arXiv:2609.36675 · cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/evolvent-ai/Godel-Forest

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• Trace — 10× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Multi-Agent Framework — 5× in this paper · explained in 3 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Agent Harness — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Post-Selection — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
• AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
• ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
• A Self-Improving Coding Agent
• Mendel G\"odel Machine: Recursive Self-Improving Coding Agents via Comparative Evolution
• RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement
• Autodata: An agentic data scientist to create high quality synthetic data
• PostTrainBench: Can LLM Agents Automate LLM Post-Training?
• Towards Active Synthetic Data Generation for Finetuning Language Models
• Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements
• SGM: A Statistical Godel Machine for Risk-Controlled Recursive Self-Modification
• Hyperagents
• The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
• Self-Rewarding Language Models
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• Qwen3 Technical Report

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering