A self-trained multi-agent RL framework pairs Verilog and Python agents for oracle-free mutual verification in RTL generation and reports 75.0% / 80.1% pass@1 on VerilogEval V2 using 4B / 9B models.
Teaching large language models to self-debug
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DAPO introduces decoupled clipping and dynamic sampling for LLM RL, achieving 50 on AIME 2024 with Qwen2.5-32B while fully open-sourcing code, data, and the verl-based training system.
The paper introduces a collaborative multi-agent framework for LLMs and applies it conceptually to existing models like Auto-GPT, BabyAGI, and Gorilla through case studies in domains such as courtroom simulations and software development.
citing papers explorer
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ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
A self-trained multi-agent RL framework pairs Verilog and Python agents for oracle-free mutual verification in RTL generation and reports 75.0% / 80.1% pass@1 on VerilogEval V2 using 4B / 9B models.
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DAPO: An Open-Source LLM Reinforcement Learning System at Scale
DAPO introduces decoupled clipping and dynamic sampling for LLM RL, achieving 50 on AIME 2024 with Qwen2.5-32B while fully open-sourcing code, data, and the verl-based training system.
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Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
The paper introduces a collaborative multi-agent framework for LLMs and applies it conceptually to existing models like Auto-GPT, BabyAGI, and Gorilla through case studies in domains such as courtroom simulations and software development.