train-orchestrator-descriptor-conditioned-routing-plans TEC-0218 · proposedTrain the orchestrator on descriptor-conditioned routing pla
Fine-tune the planning model to emit a rationale plus an ordered sub-agent plan from tool descriptors, then refine with GRPO rewarding parseable output, coverage F1 against the target plan, and satisfied precedence edges.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Fine-tune the planning model to emit a rationale plus an ordered sub-agent plan from tool descriptors, then refine with GRPO rewarding parseable output, coverage F1 against the target plan, and satisfied precedence edges. Filed by the drafting stage from FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production (2608.29814) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Exact-match chain accuracy, per-step accuracy and slot-aware edit distance on two held-out sets (DCE 522 easy, DCH 200 hard): 91.0/81.5 chain accuracy versus Gemini 3 Pro at 80.3/47.5, GPT-4o 58.0/31.0, and Qwen3-8B base 29.5/11.5; ablations show GRPO adds +33.5 chain points on DCH over the SFT checkpoint, and the frozen Director selected two post-training sub-agents correctly in 93.0% and 99.2% of cases.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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