Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability

• URL: http://arxiv.org/abs/2606.08850v1
• Date: Sun, 07 Jun 2026 21:43:37 GMT
• Title: Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
• Abstract summary: Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding.<n>However, extending ITS to tasks prone to systematic failure typically relies on costly external solvers or brittle, model-based verifiers.<n>Our pipeline applies seamlessly across broad-purpose, domain-specialized, and multimodal architectures.
• Score: 15.77992479706947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output selection. However, extending ITS to tasks prone to systematic failure - driven by faulty initial assumptions or unmet multidimensional constraints - typically relies on costly external solvers or brittle, model-based verifiers. Our key insight is that the intrinsic statistics of parallel sample sets, specifically length-adjusted tail entropy, provide a robust discriminative signal for solution quality without access to ground truth. Crucially, these statistics serve as a difficulty gate for adaptive compute allocation, dynamically routing problems across scaling regimes. First, Intrinsic Selection (iS) ranks candidates post-hoc, matching consensus-based algorithms across three domains and improving engineering design selection by 20% over pass@1 baselines. Second, Intrinsic Particle Filtering (iPF) generalizes this to step-level resampling, guiding generation toward high-confidence reasoning trajectories to improve pass@1 by 6.1 points on average on hard math problems. Finally, Particle Distillation (dPF) injects privileged guidance via early logit blending and KL-guided resampling, steering generation past systematic reasoning errors to satisfy expert rubrics, yielding up to 26.5% gains on complex clinical responses. Our pipeline applies seamlessly across broad-purpose, domain-specialized, and multimodal architectures, successfully extending ITS to open-ended domains without requiring trained reward models or exact ground-truth verification.

Related papers

• How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings? [58.740078141879984]
  Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on.<n>Recent work in causality-based domain generalization has shown how shared causal structure can induce invariant predictors.<n>In this paper, we study when full or partial causal knowledge can provably improve supervised domain adaptation.
  arXiv  Detail & Related papers  (2026-06-10T21:07:49Z)
• DISA: Offline Importance Sampling for Distribution-Matching LLM-RL [56.9445657766829]
  We introduce DISA, which moves this calibration problem outside the RL loop.<n> DISA draws proposal trajectories offline, estimates the partition function via importance sampling, and freezes the resulting partition-function estimate.<n>On two open-weight backbones across six math and three code benchmarks, DISA matches or exceeds the online-coupled distribution-matching baseline Flow.
  arXiv  Detail & Related papers  (2026-05-17T07:14:44Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning [32.332197731504046]
  Decoding strategies play a central role in shaping the reasoning ability of large language models.<n>Traditional methods such as greedy decoding and beam search often suffer from error propagation.<n>We propose an entropy-guided decoding framework that introduces token-level adaptivity into generation.
  arXiv  Detail & Related papers  (2026-03-10T23:08:26Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning [17.384089089363382]
  We identify a root cause that existing methods overlook: the uniform penalization of errors.<n>Current approaches treat all incorrect rollouts within a group identically.<n>We propose the Asymmetric Confidence-aware Error Penalty (ACE)
  arXiv  Detail & Related papers  (2026-02-24T22:46:43Z)
• R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning [15.278249213859844]
  Multi-output Gaussian process (MGP) models have attracted significant attention for their flexibility and uncertainty-quantification capabilities.<n>They have been widely adopted in multi-source transfer learning scenarios due to their ability to capture inter-task correlations.<n>This paper proposes a Double-Regularized Heterogeneous Gaussian Process framework (R2-HGP) to overcome several challenges in transfer learning.
  arXiv  Detail & Related papers  (2025-12-11T03:38:20Z)
• Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling [15.828750560145751]
  Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time.<n>PF has emerged as a strong ITS method for complex mathematical reasoning tasks.<n>It is vulnerable when guided by process reward models, which often assign overconfident scores early in the reasoning process.<n>This failure mode, known as particle impoverishment, is especially severe under constrained computational budgets.<n>We introduce Entropic Particle Filtering (ePF), an algorithm that integrates two new techniques to solve these issues.
  arXiv  Detail & Related papers  (2025-10-07T11:48:32Z)
• Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference [8.823529310904162]
  Multi-agent systems (MAS) are critical for automating complex tasks, yet their practical deployment is hampered by the challenge of failure attribution.<n>We introduce the first failure attribution framework for MAS grounded in multi-granularity causal inference.
  arXiv  Detail & Related papers  (2025-09-10T15:22:00Z)
• Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality [55.06411438416805]
  Constrained Markov Decision Processes (CMDPs) are critical in many high-stakes applications.<n>This paper introduces a novel approach, Two-Stage Deep Decision Rules (TS- DDR) to efficiently train parametric actor policies.<n>It is shown to enhance solution quality and to reduce computation times by several orders of magnitude when compared to current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-23T18:19:47Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.