github search·zihangtian-arco
zihangtian/ARCO
zihangtian/ARCO: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric-based rewards improve interpretability through natural-language criteria, but existing methods score at the trajecto...
AgentScore
9.50.00
+0.00 vs 24h ago
Adoption5.0
Quality—
Momentum60.0
Community5.0
Quality unrated · weight redistributed
CapabilitiesCode GenerationMulti Agent
DeploymentLibrary
MaturityExperimental
Score breakdown
Headline plus the four pillars over time. Click any pillar below to see the signals feeding it.
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Pillar contributions
- GitHub stars7→ 15.0MCP registry listing0→ 0.0
Pillar = mean of 2 scaled values = 5.0.
Awaiting first reading — these signals apply to this agent and will be ingested on the next tier tick: SO questions (7d), Product Hunt upvotes, Docker Hub pulls, Crates.io downloads (90d), Tech-news mentions (30d)
Not applicable — this agent doesn't have the prerequisite (no GitHub repo, no HF mirror, etc.) for these signals to ever apply: HF downloads (30d), npm weekly installs, PyPI monthly installs
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