--- title: "awesome-RLHF vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opendilab-awesome-rlhf-vs-woooodyy-llm-agent-paper-list" tools: ["opendilab-awesome-rlhf", "woooodyy-llm-agent-paper-list"] --- # awesome-RLHF vs LLM-Agent-Paper-List *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods; 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 reinforcement learning. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) reports 4.4k GitHub stars, 258 forks, and 6 open issues, last pushed May 20, 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 [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List). | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | A curated list of reinforcement learning with human feedback resources (continually updated) | Must-read papers for LLM-based agents. | | Stars | 4,422 | 8,222 | | Forks | 258 | 499 | | Open issues | 6 | 32 | | Language | - | - | | Adopt for | awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. | 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 | Evaluation & Observability, Model Training | AI Agents, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 89d | 371d | | Open issues (now) | 6 | 32 | | Stars delta | +9 (30d) | +50 (30d) | | Open issues delta | 0 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) | ## Decision facts: awesome-RLHF - **Adopt for:** awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. ## 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 awesome-RLHF if… - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. - More recently updated (last pushed May 20, 2026). ### Choose LLM-Agent-Paper-List if… - Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp. - Also covers AI Agents. - 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 awesome-RLHF - If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ## 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 awesome-RLHF and LLM-Agent-Paper-List? awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). 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 awesome-RLHF over LLM-Agent-Paper-List? Choose awesome-RLHF over LLM-Agent-Paper-List when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems; More recently updated (last pushed May 20, 2026). ### When should I choose LLM-Agent-Paper-List over awesome-RLHF? Choose LLM-Agent-Paper-List over awesome-RLHF when Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp; Also covers AI Agents; 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 awesome-RLHF? If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ### 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 awesome-RLHF or LLM-Agent-Paper-List more popular on GitHub? LLM-Agent-Paper-List has more GitHub stars (8,222 vs 4,422). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-RLHF and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-RLHF or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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/opendilab-awesome-rlhf-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, awesome-RLHF or LLM-Agent-Paper-List? awesome-RLHF: Steady. 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 awesome-RLHF and LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opendilab-awesome-rlhf`](/api/graphcanon/graph?tool=opendilab-awesome-rlhf) - 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/_