RL-trained lightweight controller using answer statistics improves trade-offs among correctness, latency, and total samples in adaptive sampling for LLM test-time scaling.
arXiv preprint arXiv:2305.11860 , year=
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C3RL is a new RL algorithm combining correctness, calibration, and reference accuracy rewards to improve LLM confidence calibration, enabling CAS to outperform majority voting with up to 12.33x lower inference cost.
Empirical evaluation on Gemini 2.5 models shows self-consistency yields only 0.4% gain on HotpotQA and 1.6% on MATH-500 across 20 samples while token costs scale linearly, with performance plateauing or declining at higher counts.
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.
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Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling
RL-trained lightweight controller using answer statistics improves trade-offs among correctness, latency, and total samples in adaptive sampling for LLM test-time scaling.
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Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling
C3RL is a new RL algorithm combining correctness, calibration, and reference accuracy rewards to improve LLM confidence calibration, enabling CAS to outperform majority voting with up to 12.33x lower inference cost.
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Self-Consistency Is Losing Its Edge: Diminishing Returns and Rising Costs in Modern LLMs
Empirical evaluation on Gemini 2.5 models shows self-consistency yields only 0.4% gain on HotpotQA and 1.6% on MATH-500 across 20 samples while token costs scale linearly, with performance plateauing or declining at higher counts.
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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.