--- title: "circuit-breakers vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/grayswanai-circuit-breakers-vs-opendilab-awesome-rlhf" tools: ["grayswanai-circuit-breakers", "opendilab-awesome-rlhf"] --- # circuit-breakers vs awesome-RLHF *GraphCanon updated Aug 17, 2026* ## Verdict Pick circuit-breakers when license: circuit-breakers is MIT, awesome-RLHF is Apache-2.0; pick awesome-RLHF when license: awesome-RLHF is Apache-2.0, circuit-breakers is MIT. [circuit-breakers](https://github.com/GraySwanAI/circuit-breakers) reports 266 GitHub stars, 42 forks, and 14 open issues, last pushed Sep 24, 2024. [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 [circuit-breakers's repository](https://github.com/GraySwanAI/circuit-breakers) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [circuit-breakers](/tools/grayswanai-circuit-breakers.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | Improving Alignment and Robustness with Circuit Breakers | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 266 | 4,422 | | Forks | 42 | 258 | | Open issues | 14 | 6 | | Language | Jupyter Notebook | - | | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [circuit-breakers](/tools/grayswanai-circuit-breakers.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 679d | 89d | | Open issues (now) | 14 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/grayswanai-circuit-breakers/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/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. ## Choose when ### Choose circuit-breakers if… - License: circuit-breakers is MIT, awesome-RLHF is Apache-2.0. - Tags unique to circuit-breakers: adversarial-attacks, alignment, circuit breaker, robustness. - If needing robust protection against adversarial attacks that do not compromise model capability ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, circuit-breakers is MIT. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - Also covers Model Training. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ## When NOT to use circuit-breakers - When the focus is on enhancing content diversity rather than filtering harmful content - In scenarios where minimizing the alteration of original model output is critical ## 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 circuit-breakers and awesome-RLHF? circuit-breakers: Improving Alignment and Robustness with Circuit Breakers. 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 circuit-breakers over awesome-RLHF? Choose circuit-breakers over awesome-RLHF when License: circuit-breakers is MIT, awesome-RLHF is Apache-2.0; Tags unique to circuit-breakers: adversarial-attacks, alignment, circuit breaker, robustness; If needing robust protection against adversarial attacks that do not compromise model capability. ### When should I choose awesome-RLHF over circuit-breakers? Choose awesome-RLHF over circuit-breakers when License: awesome-RLHF is Apache-2.0, circuit-breakers is MIT; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; Also covers Model Training; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### When should I avoid circuit-breakers? When the focus is on enhancing content diversity rather than filtering harmful content In scenarios where minimizing the alteration of original model output is critical ### 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 circuit-breakers or awesome-RLHF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 266). Stars measure visibility, not whether either tool fits your constraints. ### Are circuit-breakers and awesome-RLHF open source? Yes - both are open-source projects on GitHub (circuit-breakers: MIT, awesome-RLHF: Apache-2.0). ### Where can I find alternatives to circuit-breakers or awesome-RLHF? GraphCanon lists graph-backed alternatives at [circuit-breakers alternatives](/tools/grayswanai-circuit-breakers/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([circuit-breakers markdown twin](/tools/grayswanai-circuit-breakers/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/grayswanai-circuit-breakers-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, circuit-breakers or awesome-RLHF? circuit-breakers: Dormant. 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 circuit-breakers and awesome-RLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [circuit-breakers trust report](/tools/grayswanai-circuit-breakers/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=grayswanai-circuit-breakers`](/api/graphcanon/graph?tool=grayswanai-circuit-breakers) - 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/_