---
title: "SPIN vs SPPO"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/uclaml-spin-vs-uclaml-sppo"
tools: ["uclaml-spin", "uclaml-sppo"]
---

# SPIN vs SPPO

*GraphCanon updated Aug 24, 2026*

## Verdict

Pick SPIN if sPIN is specialized for self-play fine-tuning in large language models through deep learning; pick SPPO if sPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF.

[SPIN](https://uclaml.github.io/SPIN/) reports 1.3k GitHub stars, 106 forks, and 24 open issues, last pushed May 8, 2024. [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 [SPIN's repository](https://github.com/uclaml/SPIN) and [SPPO's repository](https://github.com/uclaml/SPPO).

| | [SPIN](/tools/uclaml-spin.md) | [SPPO](/tools/uclaml-sppo.md) |
| --- | --- | --- |
| Tagline | Official implementation of Self-Play Fine-Tuning | Official implementation of Self-Play Preference Optimization for fine-tuning large language models via RLHF |
| Stars | 1,254 | 589 |
| Forks | 106 | 48 |
| Open issues | 24 | 15 |
| Language | Python | Python |
| Adopt for | SPIN is specialized for self-play fine-tuning in large language models through deep learning. | SPPO targets fine-tuning of large language models through Self-Play Preference Optimization within RLHF. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Apache-2.0 |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [SPIN](/tools/uclaml-spin.md) | [SPPO](/tools/uclaml-sppo.md) |
| --- | --- | --- |
| Days since push | 837d | 578d |
| Open issues (now) | 24 | 15 |
| Stars delta | +6 (30d) | -1 (30d) |
| Full report | [trust report](/tools/uclaml-spin/trust.md) | [trust report](/tools/uclaml-sppo/trust.md) |

## Decision facts: SPIN

- **Adopt for:** SPIN is specialized for self-play fine-tuning in large language models through deep learning.

## 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 SPIN if…

- When implementing self-play algorithms aimed at enhancing performance of large language models within constrained domains.
- More GitHub stars (1.3k vs 589) - visibility, not fit.

### Choose SPPO if…

- Tags unique to SPPO: rlhf.
- Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences.
- More recently updated (last pushed Jan 23, 2025).

## When NOT to use SPIN

- If your project strictly adheres to frameworks that do not incorporate self-play techniques for training or fine-tuning models.
- When prioritizing a model training framework that relies on supervised learning rather than the self-play methodology SPIN is based upon.

## 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 SPIN and SPPO?

SPIN: Official implementation of Self-Play Fine-Tuning. 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 SPIN over SPPO?

Choose SPIN over SPPO when When implementing self-play algorithms aimed at enhancing performance of large language models within constrained domains; More GitHub stars (1.3k vs 589) - visibility, not fit.

### When should I choose SPPO over SPIN?

Choose SPPO over SPIN when Tags unique to SPPO: rlhf; Use if you aim to specialize in fine-tuning large language models with self-play techniques and reinforcement learning for enhancing model preferences; More recently updated (last pushed Jan 23, 2025).

### When should I avoid SPIN?

If your project strictly adheres to frameworks that do not incorporate self-play techniques for training or fine-tuning models. When prioritizing a model training framework that relies on supervised learning rather than the self-play methodology SPIN is based upon.

### 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 SPIN or SPPO more popular on GitHub?

SPIN has more GitHub stars (1,254 vs 589). Stars measure visibility, not whether either tool fits your constraints.

### Are SPIN and SPPO open source?

Yes - both are open-source projects on GitHub (SPIN: Apache-2.0, SPPO: Apache-2.0).

### Where can I find alternatives to SPIN or SPPO?

GraphCanon lists graph-backed alternatives at [SPIN alternatives](/tools/uclaml-spin/alternatives) and [SPPO alternatives](/tools/uclaml-sppo/alternatives) ([SPIN markdown twin](/tools/uclaml-spin/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/uclaml-spin-vs-uclaml-sppo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, SPIN or SPPO?

SPIN: Dormant. 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 SPIN and SPPO?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [SPIN trust report](/tools/uclaml-spin/trust); [SPPO trust report](/tools/uclaml-sppo/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=uclaml-spin`](/api/graphcanon/graph?tool=uclaml-spin)
- 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/_