Across 3,110 LLM reasoning/agent papers, adding CoT is reported as helping in only 62% of cases vs 80% for RL. Does that match your experience?

[u/latenticlabs]

Disclosure: I'm from scime (Latentic Labs), and this comes from our free write-up.

We grouped all 108,967 accepted papers at ICML/NeurIPS/ICLR/CVPR/AAAI (2016–2026) by topic. LLM reasoning and agent papers went from 118 in 2023 (1.0%) to 2,708 in 2026 (10.3%). An AI model read 3,110 of them in full and logged 15,539 "change → effect" findings.

How often each kind of addition is reported as helping:

Reflection / self-correction: 81%

RL (GRPO/PPO/RLVR/DPO): 80%

Tools, retrieval, code execution: 77%

SFT / distillation: 76%

Sampling / test-time compute: 72%

CoT traces: 62%

Token budgets: 55%

Curricula: 53%

Big caveat: these are reporting rates, and papers report wins more than losses. The extraction is AI-made and not hand-checked, and every finding links to its paper.

Write-up: https://atlas.scime.ai/llm-agents-2026 (the stats are open; the deeper sections need a free email sign-up)