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ARXIV:2604.02268 · AGENTIC LLMS · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.02268AGENTIC LLMSSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEZhengxi Lu · Zhiyuan Yao · Jinyang Wu · Chengcheng Han · Qi Gu · Xunliang Cai · +4 at arXiv
Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency.
Opportunity summary
Pain Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency.
Evidence 0 refs | 0 sources | 67% coverage
Blocker Evidence verified
Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial…
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a…
Agentic LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency.
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Paper Pack
10.48550/arXiv.2604.02268Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency.
Abstract
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi-turn task completion. 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, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
verified0 refs; 0 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes...
METHOD
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduc...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient c...
WHY NOW
Agentic LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
SKILL0 achieves substantial improvements over the standard RL baseline (+9.7% for ALFWorld and +6.6% for Search-QA)
Directly stated in abstract with clear numeric evidence
partial
SKILL0 achieves substantial improvements over the standard RL baseline (+9.7% for ALFWorld and +6.6% for Search-QA)
Directly stated in abstract with clear numeric evidence
partial
maintaining a highly efficient context of fewer than 0.5k tokens per step
Directly stated in abstract with clear numeric evidence
partial
retrieval noise introduces irrelevant guidance
Directly stated in abstract as a limitation of existing approaches
partial
injected skill content imposes substantial token overhead
Directly stated in abstract as a limitation of existing approaches
partial
SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it
Directly stated in abstract describing the method
partial
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
Directly stated in abstract describing the method, though some details require inference
partial
Skills are grouped offline by category and rendered with interaction history into a compact visual context
Directly stated in abstract describing the method
partial
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Concepts
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Internalize LLM agent skills into model parameters for zero-shot autonomous behavior, reducing token overhead and improving efficiency.
Segment
Agentic LLMs
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
Direct
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CITED BY
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Commercially relevant
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 67% evidence coverage.
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
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No budget owner is verified for this paper.
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Map target operator, economic buyer, and procurement trigger.
Defensibility
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Defensibility signals are missing.
Evidence
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
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Run cost passport or mark the cost field not applicable.
Regulatory load
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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