SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/skill0-in-context-agentic-reinforcement-learning-for-skill-internalization

stale

Proof freshness: stale
Proof status: verified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 67%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID skill0-in-context-agentic-reinforcement-learning-for-skill-internalization | Route /signal-canvas/skill0-in-context-agentic-reinforcement-learning-for-skill-internalization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/skill0-in-context-agentic-reinforcement-learning-for-skill-internalization

MCP example

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  "arguments": {
    "mode": "paper",
    "paper_ref": "skill0-in-context-agentic-reinforcement-learning-for-skill-internalization",
    "query_text": "Summarize SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization"
  }
}

source_context

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  "surface": "signal_canvas",
  "mode": "paper",
  "query": "SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization",
  "normalized_query": "2604.02268",
  "route": "/signal-canvas/skill0-in-context-agentic-reinforcement-learning-for-skill-internalization",
  "paper_ref": "skill0-in-context-agentic-reinforcement-learning-for-skill-internalization",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

PDF: https://arxiv.org/pdf/2604.02268v1

Repository: https://github.com/ZJU-REAL/SkillZero

Source count: Pending verification

Coverage: 67%

Last proof check: 2026-04-03T20:30:24.303Z

Signal Canvas receipt window

Ready for execution: SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

/buildability/skill0-in-context-agentic-reinforcement-learning-for-skill-internalization

Build Nowready

Subject: SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Verdict

Build Now

Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.

Preparing verified analysis

GitHub Code Pulse

Cached

Stars

326

Health

Last commit

5/20/2026

Forks

Open repository

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
SKILL0 achieves substantial improvements over the standard RL baseline (+9.7% for ALFWorld and +6.6% for Search-QA)
Implicationpartial
Directly stated in abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
SKILL0 achieves substantial improvements over the standard RL baseline (+9.7% for ALFWorld and +6.6% for Search-QA)
Implicationpartial
Directly stated in abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
maintaining a highly efficient context of fewer than 0.5k tokens per step
Implicationpartial
Directly stated in abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
retrieval noise introduces irrelevant guidance
Implicationpartial
Directly stated in abstract as a limitation of existing approaches
Verificationpartial
partial
Evidencepartial
injected skill content imposes substantial token overhead
Implicationpartial
Directly stated in abstract as a limitation of existing approaches
Verificationpartial
partial
Evidencepartial
SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it
Implicationpartial
Directly stated in abstract describing the method
Verificationpartial
partial
Evidencepartial
A Dynamic Curriculum then evaluates each skill file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget
Implicationpartial
Directly stated in abstract describing the method, though some details require inference
Verificationpartial
partial
Evidencepartial
Skills are grouped offline by category and rendered with interaction history into a compact visual context
Implicationpartial
Directly stated in abstract describing the method
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

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