train-only-central-coordinating-agent-freeze-sub-agents TEC-0334 · proposedTrain only the central coordinating agent, freeze sub-agents
Treats sub-agents as part of the environment and applies RL gradients only to the central agent's policy, with trajectories reconstructed from captured per-session interface call records.
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.
Treats sub-agents as part of the environment and applies RL gradients only to the central agent's policy, with trajectories reconstructed from captured per-session interface call records. Filed by the drafting stage from Harness-RL: Black-Box Reinforcement Learning with Action-Args Decoupling for Central-Agent Multi-Agent Harnesses (2608.29641) 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: Token-level F1 and accuracy across seven multi-hop QA benchmarks with Qwen2.5-3B, central-only vs all-agent joint training under the same optimization scheme.. 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.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
Code
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Sources
Whether a technique works is a claim. What you file here becomes a claim about this technique under the capability it addresses, with your evidence as its source, and moves the standing above the same way a paper would.