# openbench

> **Popularity potential 42/100 — useful.**  
> Kind: `code` · Source: `github` · Fable confidence: 90%

Reverse-engineers the RL reward a coding model was trained on, from black-box agentic behavior — tested on Claude Opus 4.8, Fable 5, and GPT-5.5.

## Scorecard

| Dimension | Score |
| --- | --- |
| Originality | 4/5 |
| Usefulness | 3/5 |
| Execution | 2/5 |
| Momentum | 1/5 |
| Fable fit | 4/5 |
| Code quality | 2/5 |

## Fable-5 evidence

- References “Fable 5” with AI context
- Confidence: **90%** 

## Signals

- Stars: 0 · Forks: 0 · Open issues: 0
- Language: Python · License: MIT
- Last pushed: 2026-06-13T21:37:29Z
- Community reviews captured: 0

## Verdict

Reverse-engineering RL reward signals from black-box behavior is a live research problem and the cross-model comparison angle (Opus 4.8, Fable 5, GPT-5.5) makes results inherently shareable in alignment and eval circles. However, the project launched today with zero traction, no community scaffolding (no topics, no issues), and no visible adoption path — breakout is entirely contingent on the methodology surviving peer scrutiny. Strong Fable 5 fit for this catalog, but reads as a research artifact in progress rather than a tool with momentum.

## Links

- Repo: https://github.com/BrandeisPatrick/openbench
- Discovered at: https://github.com/BrandeisPatrick/openbench

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_Rated by claude-sonnet-4-6 · 2026-06-13T21:44:32.409Z · fable.directory_

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