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RLHF-LLM-Optimization

Full RLHF pipeline — SFT, reward modeling, PPO with KL divergence constraints. 68% win rate vs SFT baseline, 96% safety compliance.

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50poor
▣ Score BreakdownMCPScore = Σ(raw × weight)
DimensionRawWeighted
Security
35%
100
35.0
Freshness
25%
30
7.5
Adoption
20%
0
0.0
Quality
10%
20
2.0
Trust
10%
50
5.0
Total
49.5
⚿ Capabilities & Risk Explainer
fs readfs writenetworkevalsecrets
◆ Risk level: high
fs read + fs write + network + eval + secrets active — can execute code, access credentials, and make external network calls.
⚙ Install config
Source-only — no published npm / pypi package detected.
Clone and follow the build instructions in the repo: github.com/rehan243/RLHF-LLM-Optimization
📈 Score historylast 11 snapshots
5/28/20266/6/2026 · 11 snapshots
⚙ Maintenance health
maintenance data not yet available — check back later.
⛁ Raw data
weekly downloads0
github stars0
forks0
open issues0
license✗ missing
readme length0 chars
last updated10d ago
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RLHF-LLM-Optimization — MCP Score: 50/100 | MCPScore | Timeahead