--- title: "ROLL vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-opendilab-awesome-rlhf" tools: ["alibaba-roll", "opendilab-awesome-rlhf"] --- # ROLL vs awesome-RLHF *GraphCanon updated Aug 17, 2026* ## Verdict Pick ROLL if efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided; 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. [ROLL](https://alibaba.github.io/ROLL/) reports 3.4k GitHub stars, 304 forks, and 120 open issues, last pushed Aug 7, 2026. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) has 4.4k stars, 258 forks, and 6 open issues, last pushed May 20, 2026. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [ROLL](/tools/alibaba-roll.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | Scaling Library for Reinforcement Learning with Large Language Models | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 3,354 | 4,422 | | Forks | 304 | 258 | | Open issues | 120 | 6 | | Language | Python | - | | Adopt for | Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 89d | | Open issues (now) | 120 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | ## Decision facts: ROLL - **Adopt for:** Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. ## 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. ## Choose when ### Choose ROLL if… - Tags unique to ROLL: agentic, rlvr. - When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. - More recently updated (last pushed Aug 7, 2026). ### Choose awesome-RLHF if… - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. - More GitHub stars (4.4k vs 3.4k) - visibility, not fit. ## When NOT to use ROLL - Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. - Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ## 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. ## Common questions ### What is the difference between ROLL and awesome-RLHF? ROLL: Scaling Library for Reinforcement Learning with Large Language Models. awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). See the comparison table for live GitHub stats and shared categories. ### When should I choose ROLL over awesome-RLHF? Choose ROLL over awesome-RLHF when Tags unique to ROLL: agentic, rlvr; When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions; More recently updated (last pushed Aug 7, 2026). ### When should I choose awesome-RLHF over ROLL? Choose awesome-RLHF over ROLL when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems; More GitHub stars (4.4k vs 3.4k) - visibility, not fit. ### When should I avoid ROLL? Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ### 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. ### Is ROLL or awesome-RLHF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 3,354). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and awesome-RLHF open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, awesome-RLHF: Apache-2.0). ### Where can I find alternatives to ROLL or awesome-RLHF? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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/alibaba-roll-vs-opendilab-awesome-rlhf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or awesome-RLHF? ROLL: Very active. awesome-RLHF: Steady. 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 ROLL and awesome-RLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll) - 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/_