--- title: "OpenRLHF vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openrlhf-openrlhf-vs-woooodyy-llm-agent-paper-list" tools: ["openrlhf-openrlhf", "woooodyy-llm-agent-paper-list"] --- # OpenRLHF vs LLM-Agent-Paper-List *GraphCanon updated Sep 20, 2026* ## Verdict Pick OpenRLHF if openRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters; pick LLM-Agent-Paper-List if lLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through. [OpenRLHF](https://openrlhf.readthedocs.io/) reports 10k GitHub stars, 1.0k forks, and 380 open issues, last pushed Sep 17, 2026. [LLM-Agent-Paper-List](https://arxiv.org/abs/2309.07864) has 8.2k stars, 499 forks, and 32 open issues, last pushed Sep 12, 2025. Figures are from public GitHub metadata via [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List). | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | Scalable and High-performance Agentic RL Framework based on Ray | Must-read papers for LLM-based agents. | | Stars | 10,014 | 8,222 | | Forks | 1,019 | 499 | | Open issues | 380 | 32 | | Language | Python | - | | Adopt for | OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. | LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving, Model Training | AI Agents, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 371d | | Open issues (now) | 380 | 32 | | Stars delta | +123 (30d) | +50 (30d) | | Open issues delta | +13 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/openrlhf-openrlhf/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) | ## Decision facts: OpenRLHF - **Adopt for:** OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. ## Decision facts: LLM-Agent-Paper-List - **Adopt for:** LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. ## Choose when ### Choose OpenRLHF if… - Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement-learning-from-human-feedba, transformers. - Also covers Inference & Serving. - When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### Choose LLM-Agent-Paper-List if… - Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp. - Also covers AI Agents, Evaluation & Observability. - When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning ## When NOT to use OpenRLHF - If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. - When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ## When NOT to use LLM-Agent-Paper-List - If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents - When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications - If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ## Common questions ### What is the difference between OpenRLHF and LLM-Agent-Paper-List? OpenRLHF: Scalable and High-performance Agentic RL Framework based on Ray. LLM-Agent-Paper-List: Must-read papers for LLM-based agents.. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenRLHF over LLM-Agent-Paper-List? Choose OpenRLHF over LLM-Agent-Paper-List when Tags unique to OpenRLHF: proximal-policy-optimization, raylib, reinforcement-learning-from-human-feedba, transformers; Also covers Inference & Serving; When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### When should I choose LLM-Agent-Paper-List over OpenRLHF? Choose LLM-Agent-Paper-List over OpenRLHF when Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp; Also covers AI Agents, Evaluation & Observability; When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning. ### When should I avoid OpenRLHF? If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ### When should I avoid LLM-Agent-Paper-List? If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ### Is OpenRLHF or LLM-Agent-Paper-List more popular on GitHub? OpenRLHF has more GitHub stars (10,014 vs 8,222). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenRLHF and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to OpenRLHF or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([OpenRLHF markdown twin](/tools/openrlhf-openrlhf/alternatives.md), [LLM-Agent-Paper-List markdown twin](/tools/woooodyy-llm-agent-paper-list/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/openrlhf-openrlhf-vs-woooodyy-llm-agent-paper-list.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenRLHF or LLM-Agent-Paper-List? OpenRLHF: Very active. LLM-Agent-Paper-List: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for OpenRLHF and LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openrlhf-openrlhf`](/api/graphcanon/graph?tool=openrlhf-openrlhf) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_