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ARXIV:2603.28716 · AGENTIC RL · SUBMITTED 31 MAR · 20:21 UTC · FRESHNESS STALE
ARXIV:2603.28716AGENTIC RLSUBMITTED 31 MAR · 20:21 UTCFRESHNESS STALESongjun Tu · Chengdong Xu · Qichao Zhang · Yaocheng Zhang · Xiangyuan Lan · Linjing Li · +1 at arXiv
A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills.
Opportunity summary
Pain A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills.
Evidence 12 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable…
Agentic reinforcement learning (RL) can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills…
Agentic RL moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Score4.0Analysis summary
A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills.
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Paper Pack
10.48550/arXiv.2603.28716A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills.
Abstract
Agentic reinforcement learning (RL) can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Built entirely from training-time experience, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Experiments on ALFWorld and WebShop with Qwen2.5-7B-Instruct and Qwen3-4B-Instruct-2507 show that D2Skill consistently improves success rates over skill-free baselines by 10-20 points. Further ablations and analyses show that both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains, while the learned skills exhibit higher utility, transfer across evaluation settings, and introduce only modest training overhead.
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Proof status
unverified12 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-le...
METHOD
Agentic reinforcement learning (RL) can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-gra...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and e...
WHY NOW
Agentic RL moved forward this cycle; last verified April 2026. Public score 4.0/10.
D2Skill achieves 10–20 point gains in success rate over skill-free baselines (GRPO)
Directly stated in abstract with specific numeric range and supported by results table showing gains of 15.7-18.8 points.
partial
organizes reusable experience into task skills for high-level guidance and step skills for fine-grained interaction support.
Explicitly stated as core method contribution in multiple sections of the paper.
partial
skills expanded through reflection and maintained via utility-guided retrieval and pruning
Directly stated as a core method contribution and described in the framework diagram.
partial
D2Skill acquires and maintains its skill bank using only training-time experience, while still achieving better performance
Explicitly stated comparison with SkillRL method, highlighting D2Skill's advantage.
partial
D2Skill reaches 92.2 on ALFWorld, nearly matching GRPO trained for longer
Specific numeric result stated in the analysis section, though exact context details are limited.
partial
both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains
Directly stated in abstract as conclusion from ablations and analyses.
partial
performance gap between the two groups is used to construct hindsight signals for policy optimization and skill utility updates
Explicitly described as core training mechanism in method section and framework diagram.
partial
the learned skills exhibit higher utility, transfer across evaluation settings
Stated in abstract as finding from analyses, though specific evidence quotes are limited.
partial
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Concepts
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Materials
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Competitors
A dynamic skill bank for agentic reinforcement learning that improves performance by organizing reusable experience into task and step skills.
Segment
Agentic RL
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
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CITED BY
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
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
12 refs / 3 sources / 50% coverage
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
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
12 references, 3 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
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Defensibility
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Defensibility signals are missing.
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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
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
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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Operator workflow not sourced.
No buyer or workflow interview attached.
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People
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Regulatory need unclassified.
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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