mlsanityData to 14 Oct 2026

GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design

arXiv 2605.14215 ↗First posted 14 May 2026cs.AI cs.LG q-bio.QMcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Genetic circuit design remains a laborious, expert-driven process despite decades of progress in synthetic biology. We study this problem through code generation: models produce Python code in pysbol3 to construct genetic circuits in the Synthetic Biology Open Language (SBOL), a formal representation that supports automated verification. We introduce GenCircuit-RL, a reinforcement learning framework built around hierarchical verification rewards that decompose correctness into five levels, from code execution to task-specific topological checks, and a four-stage curriculum that shifts optimization pressure from code generation to functional reasoning. We also introduce SynBio-Reason, a benchmark of 4,753 circuits spanning six canonical circuit types and nine tasks from code repair to de novo design, with held-out biological parts for out-of-distribution evaluation. Hierarchical verification improves task success on functional reasoning tasks by 14 to 16 percentage points over binary rewards, and curriculum learning is required for strong design performance. The resulting models generate topologically correct circuits, generalize to novel biological parts, and rediscover canonical designs from the synthetic biology literature.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 110 references that are in the map. First posted on arXiv 14 May 2026.

Builds on

13 of 110 references.

References · 13

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2025Gemma 3 Technical Reportcited by 1,824
  6. 2022CodeT: Code Generation with Generated Testscited by 384
  7. 2025ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Modelscited by 194
  8. 2025Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domainscited by 156
  9. 2025SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyondcited by 50
  10. 2024AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Modelscited by 18
  11. 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generationcited by 11
  12. 2026Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generationcited by 5
1 more
  1. 2025RL-Struct: A Lightweight Reinforcement Learning Framework for Reliable Structured Output in LLMscited by 3

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  1. 2026EvolveGen: Algorithmic Level Hardware Model Checking Benchmark Generation through Reinforcement Learning
  2. 2026Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
  3. 2026GRPO with State Mutations: Improving LLM-Based Hardware Test Plan Generation
  4. 2025Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism
  5. 2025SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
  6. 2025ReVeal: Self-Evolving Code Agents via Reliable Self-Verification
  7. 2026CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning
  8. 2025VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation