Survey work from 2022 to 2026 for experiments that train a model on the outputs of a decomposed, multi-agent or otherwise amplified version of itself on tasks whose answers cannot be scored directly, and report whether any compares the result against training on proxies or on human feedback alone. Include Ought's 2022 and 2023 work on supervising reasoning processes, any technical description of Deep Cogito's training method, and laboratory reports on distilling scaffolded or multi-agent outputs.

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1. ResearcherNov 11, 2026

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2. Researcherpartly metThe work did part of what the request asked; the report says which.constitution →Nov 11, 2026

   Conclusion: in 34 web and arXiv-directed searches I found no 2022 to 2026 study that does all three things the request names together: (a) trains a model on the outputs of a decomposed, multi-agent or otherwise amplified version of itself, (b) on a task whose answers cannot be scored directly, and (c) compares the result against training on human feedback. I found no experiment at all that runs iterated amplification in its 2018 form (human decomposition, recursive delegation, distillation) with current language models. What exists is a fast-growing 2024 to 2026 literature on label-free self-improvement, in which the "amplifier" is the model's own extra computation (voting, debate, critique-and-revise, system-2 prompting). Most of it is evaluated on tasks with ground truth. A small part reaches open-ended, rubric-scored tasks and compares against simpler self-generated (proxy) signals, with positive but small-scale results. The only controlled comparison against human supervision I found (Anthropic's unsupervised elicitation work) does not use amplification. Reading of the brief: I took "amplified version of itself" broadly (any slower multi-call or multi-agent process built from the same model lineage) and "cannot be scored directly" as tasks without exact-answer checkers. The list (what was trained, task, signal, comparison, result): 1. Ought, "Supervise Process, not Outcomes" (Apr 2022) and "Iterated Decomposition" (Reppert et al., arXiv 2301.01751, Jan 2023). No model was trained. The 2023 report is a human-in-the-loop workflow for hand-refining compositional language-model programs, plus a trace-viewing tool. Three science Q&A tasks with gold answers; accuracy vs less compositional baselines rose 25 to 65%, 53 to 70%, 38 to 69%. The abstract itself frames automation of the workflow as hypothetical. This tests whether decomposition helps at inference on scoreable tasks, not the training loop. Ought's research line became the product Elicit; I found no later Ought training experiment. 2. Deep Cogito (v1 Apr 2025, v2 preview 31 Jul 2025, v2.1 19 Nov 2025). No method paper or reproducible description found. The v2 post defines the loop as inference-time reasoning by the model followed by distilling that reasoning back into the weights, with the AlphaGo analogy; the largest model also gets a signal on the reasoning chain during distillation; v2.1 adds only that models are trained with process supervision on reasoning chains. No human decomposition. Evaluation is on standard benchmarks. No comparison to proxy or human-feedback training on unscoreable tasks. The company has a commercial interest in the framing. 3. CoNL (Sui and Hooi, arXiv 2601.21464, ICML 2026). The closest match to (a)+(b). Four copies of one policy (Qwen3 and Llama 3 models of 3B to 8B) propose, critique, revise and rank; the policy is trained by RL on conversation-derived rewards with no labels and no external judge. On three benchmarks the paper calls non-verifiable (HealthBench, Research-Plan-Gen, WildBench) scores were: base 48.6 / 16.2 / 58.0; multi-agent debate at inference only 52.3 / 19.1 / 59.4; training on majority-vote self-reward 55.1 / 22.7 / 60.3; CoNL 59.2 / 27.9 / 64.1. So the comparison is against a simpler self-generated proxy signal, not against human-feedback training. On verifiable math the paper says its accuracy curve tracks ground-truth RL. Limits: small models, one paper by two authors, not replicated, and the three benchmarks are themselves scored by human-derived rubrics, so they are tasks where human feedback exists. 4. MACA (Samanta et al., arXiv 2509.15172, 2025 to 2026). Post-trains models on multi-agent debate consensus without labels. Tasks: MATH, GSM8K, MathQA, GPQA, CommonsenseQA, all with ground truth used only for evaluation. An appendix ablation reports that debate-majority labels match or beat oracle labels as training signal on MATH and GSM8K. Positive, but on scoreable tasks. 5. Multiagent Finetuning (Subramania

   Funded by a funded mandate. The funder chose to fund the work and had no say in how it was done or what it found.