🛰️ Daily AI Frontier
‹ back to 2026-08-01

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

Research LLM Agents

Ranking

Overall 75
Content 90
Popularity 39

Observed public metrics from 1 member.

Merged summary

TL;DR - A controlled study finds that repeated answer sampling matches or outperforms self-refinement, reflection, debate, and selection methods when total generated-token costs are equal. This suggests many reported reasoning gains may come from extra inference compute rather than self-inspection itself.

  • Evaluated seven methods across 1.5B, 3B, and 7B models on two 150-question mathematics benchmarks.
  • None of 36 paired comparisons reliably beat repeated sampling at equal token cost; 10 were reliably worse.
  • All 18 self-inspection comparisons were negative, with Self-Refine and forced Reflexion trailing by 3.6–10.1 points even at 7B.
  • Model-based Best-of-N selection improved with scale, but still did not outperform majority voting; the smallest model’s Reflexion implementation never triggered a retry.

Sources (1)

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

arXiv cs.CL Iliya Mirzaei 2026-07-30 arXiv:2607.28576
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-17 09:50:15.432181 UTC

TL;DR - A controlled study finds that repeated answer sampling matches or outperforms self-refinement, reflection, debate, and selection methods when total generated-token costs are equal. This suggests many reported reasoning gains may come from extra inference compute rather than self-inspection itself.

  • Evaluated seven methods across 1.5B, 3B, and 7B models on two 150-question mathematics benchmarks.
  • None of 36 paired comparisons reliably beat repeated sampling at equal token cost; 10 were reliably worse.
  • All 18 self-inspection comparisons were negative, with Self-Refine and forced Reflexion trailing by 3.6–10.1 points even at 7B.
  • Model-based Best-of-N selection improved with scale, but still did not outperform majority voting; the smallest model’s Reflexion implementation never triggered a retry.
item →