Inference-Time Scaling for Diffusion Language Models via Trajectory Refinement

arXiv CS · · 2 min read · Engineering & Technology

Read research and analysis on Inference-Time Scaling for Diffusion Language Models via Trajectory Refinement published by ICANEWS, a global research journal for emerging researchers.

Key Takeaways

  • PG-DLM introduces 'refinement iterations' as a new scaling axis for diffusion language models.
  • PG-DLM enables adaptive compute allocation, performing additional iterations only when needed.
  • PG-DLM empirically outperforms prior methods on reward-guided generation tasks.
  • On GSM8K, PG-DLM achieved 90.07% accuracy with 2.9 particles and 94.47% accuracy with 24.8 particles.

Why This Matters

This research provides a method for improving the performance of discrete diffusion models in language generation without retraining. The ability to adaptively allocate compute and the introduction of a new scaling axis offer pathways for more efficient and effective inference-time control in reward-guided generation.

Overview

This work investigates inference-time control for discrete diffusion models used in language generation. Specifically, it introduces Particle Gibbs Sampling for Diffusion Language Models (PG-DLM), an algorithm designed to refine generation trajectories towards desired rewards without necessitating model retraining. PG-DLM operates by constructing a Markov chain over full denoising trajectories and employing a conditional sequential Monte Carlo kernel for resampling.

Research Context

Discrete diffusion models have emerged as strong alternatives to autoregressive language models, demonstrating comparable performance through large-scale training. However, the domain of inference-time control for these models remains less explored. Existing methods for steering generation towards desired rewards typically involve resampling or filtering within a single denoising trajectory, optimizing rewards on a step-by-step basis, rather than through a trajectory-level refinement process.

Approach

The proposed PG-DLM algorithm is an inference-time technique that facilitates trajectory-level refinement. It achieves this by creating a Markov chain across complete denoising trajectories. Within this framework, a conditional sequential Monte Carlo kernel is applied to resample these trajectories. This approach introduces 'number of refinement iterations' as a new scaling axis for inference, a capability not present in prior methodologies. The method also allows for adaptive compute allocation, performing additional iterations only when required, which can lead to efficiency gains. The theoretical underpinnings of PG-DLM include derived guarantees for convergence and variance bounds, alongside an analysis of trade-offs associated with different scaling axes.

Findings

  • PG-DLM introduces a new scaling axis: the number of refinement iterations. This axis remains effective for increasing performance even when gains from adding more parallel samples begin to saturate.
  • The algorithm supports adaptive compute allocation, executing additional iterations only when necessary, which contributes to efficiency improvements.
  • The research provides theoretical guarantees for the convergence of PG-DLM and establishes variance bounds.
  • Analysis of trade-offs across different scaling axes was performed.
  • Empirically, PG-DLM demonstrated superior performance compared to prior methods across various compute budgets in reward-guided generation tasks.
  • On the GSM8K dataset, PG-DLM achieved an accuracy of 90.07% with an average of 2.9 particles.
  • Further, on GSM8K, it attained 94.47% accuracy with an average of 24.8 particles.

Research Information

Institution
arXiv CS
Original Study
View Publication
Source
arXiv CS

About ICANEWS

ICANEWS is a global research journal for emerging researchers, publishing student and emerging researcher work across all fields.