Last updated: 11 August 2026
Reinforcement learning tooling now spans two buying decisions. Teams can choose an open-source framework for direct control, or a managed platform for reinforcement fine-tuning, distributed training and production operations.
The brands that appear in AI answers reflect that split. Established framework names lead discovery, while managed fine-tuning vendors take a growing share of commercial questions.
Methodology
We track 55 buyer questions across ChatGPT, Gemini and Google AI Overview. The figures below draw on 383 completed answers collected on 11 August 2026. We record what each engine returns, not what it is asked.
ChatGPT returned 135 answers, Gemini returned 135, and Google AI Overview returned 113 at the frozen capture.
Named in answers shows the share of completed answers that mention a brand. Share of the answer shows the brand's share of all measured brand mentions. Average position shows where the brand appears when named, with a lower number meaning a higher place.
The 2026 leaderboard
| Rank | Brand | Named in answers | Share of the answer | Average position |
|---|---|---|---|---|
| 1 | Ray RLlib | 15.93% | 32.32% | 2.9 |
| 2 | Stable-Baselines3 | 11.23% | 22.43% | 1.8 |
| 3 | Together AI | 7.31% | 8.06% | 2.2 |
| 4 | Fireworks AI | 4.44% | 8.78% | 1.9 |
| 5 | Anyscale | 4.44% | 10.14% | 2.8 |
| 6 | CleanRL | 4.18% | 5.59% | 2.3 |
| 7 | AgileRL | 2.35% | 2.79% | 4.1 |
| 8 | OpenPipe | 2.35% | 4.79% | 1.0 |
| 9 | Tianshou | 1.83% | 5.11% | 2.6 |
Framework familiarity still drives discovery
Ray RLlib and Stable-Baselines3 take 54.75% of measured brand mentions between them. Both answer a clear early-stage job: choosing a library with known algorithms, examples and community support.
Ray extends that position into distributed training, multi-agent work and production infrastructure. Stable-Baselines3 wins on the familiar route from first experiment to a reliable baseline.
Managed fine-tuning platforms have entered the same shortlist
Together AI, Fireworks AI, Anyscale and OpenPipe sit in the same answers as the framework brands. They do not all sell the same product, but they compete for the same budget once a team asks whether to build training infrastructure or buy it.
This changes the category. A framework comparison now needs to address data location, evaluator design, GPU cost, deployment and production support, not just algorithm coverage.
Position and presence tell different stories
OpenPipe averages position 1.0 when named, yet appears in 2.35% of answers. AgileRL has the same visibility but averages position 4.1.
This is why a single rank cannot describe category strength. Presence shows whether a brand reaches the shortlist. Position shows how the answer treats it once it gets there.
Technical evidence carries the category
arXiv appeared in 27.42% of completed answers, GitHub in 15.4%, and technical documentation from Read the Docs in 8.88%.
The strongest brands join product claims to code, methods and examples that an engineer can inspect. The source set includes RLlib, Stable-Baselines3, CleanRL, Tianshou and AgileRL.
Practitioner channels shape implementation choices
Medium appeared in 16.97% of answers, YouTube in 9.66%, LinkedIn in 9.14%, and Reddit in 6.01%.
These channels carry practical questions that vendor pages often miss: why training fails to converge, how libraries compare on the same workload, and what teams need before a model reaches production.
The most useful contributions show the setup, result and limit. A generic product post adds less evidence than a benchmark another team can rerun.
Paid placement has reached RL questions
We saw 23 advertisers across 36 of the 55 tracked questions. The advertisers come from a broad AI and software market rather than a settled group of RL specialists.
That is a category signal. The questions have commercial value before any specialist vendor has taken a clear paid position.
What will change the next leaderboard
The next gains will come from four linked assets:
- Clear comparison pages that separate library, platform and infrastructure decisions.
- Task-specific implementation guides with runnable code.
- Independent or customer-authored tests on the same workload.
- Useful answers in the technical communities engineers already retrieve.
The framework alone does not win the answer. A public body of repeatable evidence makes sure the framework gets considered.
If you want the same category view, book 20 minutes with Marco.
