Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters
Snell et al., 2025, Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters (The Thirteenth International Conference on Learning Representations (ICLR 2025))
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6.1 Setup: Training and Using Revision Models
- How was the revision model trained and used?From 64 parallel samples per question, each correct answer was paired with 0–4 incorrect answers chosen to be similar to it (by edit distance) as context, and the model was fine-tuned to produce the correct one. Because it also turned about 38% of correct answers into wrong ones, the final answer is chosen from the revision chain by verifier or majority vote.
- Snell et al., 2025, Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters (The Thirteenth International Conference on Learning Representations (ICLR 2025)), §6.1
Source
- Charlie Snell, Jaehoon Lee, Kelvin Xu and Aviral Kumar. 2025. Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters. In The Thirteenth International Conference on Learning Representations (ICLR 2025). https://arxiv.org/abs/2408.03314