Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI research teams and post-training engineers who need a serious RL framework for trillion-parameter model training, for teams already using SGLang for inference who want a paired training runtime that shares the same rollout surface, for organizations standardizing on Apache-2.0 infrastructure (rather than BSL-licensed alternatives like some competitor RL stacks), and for anyone
Who should use it
Who should skip it
Hold off on radixark/miles if the setup requirements exceed what your current workflow or team can support without dedicated engineering time.
About this signal
radixark/miles is tracked by RepoRadar as a framework in the Training & Post-Training section. First seen 2026-08-12; the source record was last checked on 2026-08-12. The current verdict is 'try now' with a Gold tier and hard setup difficulty. The standout signals for radixark/miles are workflow potential (9.5) and momentum (9.0), while setup ease (4.2) trails — that balance shapes where it fits best. This page summarizes the public evidence on the linked source page and states where additional review is still needed.
How this item is evaluated
The radixark/miles record combines a 8.4/10 composite score with separate popularity (2.0), risk (none), and setup (hard) signals. See the scoring methodology for the current weights and evidence definitions.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
No inherent user-impacting risk is flagged from the captured evidence.