QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
Score
60Why selected
Strong novelty in addressing redundancy in test-time compute scaling via quasi-Monte Carlo, rigorous experiments with clear gains, and practical implications for efficient LLM inference and RL.
AI commentary
「This method significantly improves sample efficiency without extra inference cost, potentially reducing computational overhead for large-scale language model deployment and accelerating reinforcement learning training.」
Quick overview
Scaling inference compute by generating many independent attempts per problem is a reliable but costly lever for improving language models, leading to redundant solutions. QuasiMoTTo uses quasi-Monte Carlo to generate correlated but exact samples as a drop-in replacement for i.i.d. samples, improving sample efficiency without sacrificing parallelism. Across four reasoning benchmarks, QuasiMoTTo matches i.i.d. pass@k accuracy with 25-47% fewer samples, and in policy-gradient RL (GRPO) it matches i.i.d. performance with 50% fewer training steps. By spreading samples more evenly over the output space, it reduces redundancy and strengthens the learning signal per batch.
- Proposes correlated sampling to replace i.i.d. sampling, reducing output redundancy via quasi-Monte Carlo
- Matches i.i.d. pass@k accuracy with 25-47% fewer samples on four reasoning benchmarks
- Reduces training steps by 50% in policy-gradient RL (GRPO) while matching i.i.d. performance
- Often saturates an upper bound on pass@k that holds for any marginal-preserving sampler
Full text
Computer Science > Machine Learning
arXiv:2607.01179v1 (cs) [Submitted on 1 Jul 2026]Title:QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
Authors:Michael Y. Li, Anthony Zhan, Kanishk Gandhi, Noah D. Goodman, Emily B. Fox View a PDF of the paper titled QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling, by Michael Y. Li and 4 other authors View PDF HTML (experimental)Abstract:Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are generated independently, wasting inference compute on redundant solutions. This waste seems unavoidable. After all, independence is what makes parallel sampling trivial to scale. However, this tradeoff is not fundamental: there is a rich design space of samplers that generate correlated but exact samples entirely in parallel. We explore this design space as an avenue for improving sample efficiency in scaling inference compute and reinforcement learning (RL). Concretely, we introduce QuasiMoTTo, which uses correlated samples as a drop-in replacement for i.i.d. samples. To generate these samples, QuasiMoTTo uses a reparameterization of autoregressive sampling as inverse-CDF sampling and draws the underlying uniforms with quasi-Monte Carlo (QMC); because QMC spreads the uniforms out more evenly than i.i.d., the resulting samples cover the output space with far less redundancy. Even though the batch is correlated, each sample is marginally distributed according to the language model, so we can use the batch for policy-gradient training. Our empirical analysis focuses on understanding how efficiently QuasiMoTTo can turn compute into performance. To evaluate correlated samplers, whose dependence breaks standard pass@k estimators, we first develop an unbiased bootstrap estimator. Across four reasoning benchmarks, QuasiMoTTo matches i.i.d. pass@k accuracy with 25-47% fewer samples. Strikingly, QuasiMoTTo often saturates an upper bound on pass@k that holds for any marginal-preserving sampler. We also apply QuasiMoTTo to policy-gradient RL (GRPO) where it matches i.i.d. performance with 50% fewer training steps. These gains come from higher coverage, which yields a stronger learning signal per batch.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.01179 [cs.LG] |
| (or arXiv:2607.01179v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01179 Focus to learn more arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Michael Li [view email][v1] Wed, 1 Jul 2026 17:10:08 UTC (492 KB)
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