--- title: "optuna vs SPPO" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/optuna-optuna-vs-uclaml-sppo" tools: ["optuna-optuna", "uclaml-sppo"] --- # optuna vs SPPO *GraphCanon updated Aug 24, 2026* ## Verdict Pick optuna if optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks; pick SPPO if sPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. [optuna](https://optuna.org) reports 15k GitHub stars, 1.4k forks, and 16 open issues, last pushed Aug 3, 2026. [SPPO](https://uclaml.github.io/SPPO/) has 589 stars, 48 forks, and 15 open issues, last pushed Jan 23, 2025. Figures are from public GitHub metadata via [optuna's repository](https://github.com/optuna/optuna) and [SPPO's repository](https://github.com/uclaml/SPPO). | | [optuna](/tools/optuna-optuna.md) | [SPPO](/tools/uclaml-sppo.md) | | --- | --- | --- | | Tagline | A hyperparameter optimization framework | Official implementation of Self-Play Preference Optimization for fine-tuning large language models via RLHF | | Stars | 14,603 | 589 | | Forks | 1,361 | 48 | | Open issues | 16 | 15 | | Language | Python | Python | | Adopt for | Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. | SPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [optuna](/tools/optuna-optuna.md) | [SPPO](/tools/uclaml-sppo.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 578d | | Open issues (now) | 16 | 15 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/optuna-optuna/trust.md) | [trust report](/tools/uclaml-sppo/trust.md) | ## Decision facts: optuna - **Adopt for:** Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. ## Decision facts: SPPO - **Pricing:** freemium - **Adopt for:** SPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. - **License detail:** Apache-2.0 ## Choose when ### Choose optuna if… - License: optuna is MIT, SPPO is Apache-2.0. - Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel. - When you need to streamline the hyperparameter tuning process for machine learning models built in Python. ### Choose SPPO if… - License: SPPO is Apache-2.0, optuna is MIT. - Tags unique to SPPO: deep-learning, fine-tuning, large language models, rlhf. - Also covers LLM Frameworks. - Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences. ## When NOT to use optuna - If your project is not compatible with Python, as Optuna does not support other languages directly out of box. - Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ## When NOT to use SPPO - Avoid SPPO if your project does not require or benefit from reinforcement learning mechanisms or the fine-tuning specifics provided through self-play methods. - Do not use SPPO in scenarios where simpler model tuning approaches without self-play are adequate for achieving project goals, as it might introduce unnecessary complexity. ## Common questions ### What is the difference between optuna and SPPO? optuna: A hyperparameter optimization framework. SPPO: Official implementation of Self-Play Preference Optimization for fine-tuning large language models via RLHF. See the comparison table for live GitHub stats and shared categories. ### When should I choose optuna over SPPO? Choose optuna over SPPO when License: optuna is MIT, SPPO is Apache-2.0; Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel; When you need to streamline the hyperparameter tuning process for machine learning models built in Python. ### When should I choose SPPO over optuna? Choose SPPO over optuna when License: SPPO is Apache-2.0, optuna is MIT; Tags unique to SPPO: deep-learning, fine-tuning, large language models, rlhf; Also covers LLM Frameworks; Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences. ### When should I avoid optuna? If your project is not compatible with Python, as Optuna does not support other languages directly out of box. Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ### When should I avoid SPPO? Avoid SPPO if your project does not require or benefit from reinforcement learning mechanisms or the fine-tuning specifics provided through self-play methods. Do not use SPPO in scenarios where simpler model tuning approaches without self-play are adequate for achieving project goals, as it might introduce unnecessary complexity. ### Is optuna or SPPO more popular on GitHub? optuna has more GitHub stars (14,603 vs 589). Stars measure visibility, not whether either tool fits your constraints. ### Are optuna and SPPO open source? Yes - both are open-source projects on GitHub (optuna: MIT, SPPO: Apache-2.0). ### Where can I find alternatives to optuna or SPPO? GraphCanon lists graph-backed alternatives at [optuna alternatives](/tools/optuna-optuna/alternatives) and [SPPO alternatives](/tools/uclaml-sppo/alternatives) ([optuna markdown twin](/tools/optuna-optuna/alternatives.md), [SPPO markdown twin](/tools/uclaml-sppo/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/optuna-optuna-vs-uclaml-sppo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, optuna or SPPO? optuna: Very active. SPPO: 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 optuna and SPPO? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [optuna trust report](/tools/optuna-optuna/trust); [SPPO trust report](/tools/uclaml-sppo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=optuna-optuna`](/api/graphcanon/graph?tool=optuna-optuna) - 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/_