Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking (2510.03149v1)

Abstract: Test-time algorithms that combine the generative power of LLMs with process verifiers that assess the quality of partial generations offer a promising lever for eliciting new reasoning capabilities, but the algorithmic design space and computational scaling properties of such approaches are still opaque, and their benefits are far from apparent when one accounts for the cost of learning a high-quality verifier. Our starting point is the observation that seemingly benign errors in a learned verifier can lead to catastrophic failures for standard decoding techniques due to error amplification during the course of generation. We then ask: can this be improved with more sophisticated decoding strategies? We introduce a new process-guided test-time sampling algorithm, VGB, which uses theoretically grounded backtracking to achieve provably better robustness to verifier errors. VGB interprets autoregressive generation as a random walk on a tree of partial generations, with transition probabilities guided by the process verifier and base model; crucially, backtracking occurs probabilistically. This process generalizes the seminal Sinclair-Jerrum random walk (Sinclair & Jerrum, 1989) from the literature on approximate counting and sampling in theoretical computer science, and a conceptual contribution of our work is to highlight parallels with this literature. Empirically, we demonstrate on both synthetic and real language modeling tasks that VGB outperforms baselines on a variety of metrics.

• The paper introduces VGB, a value-guided backtracking method that revisits earlier decisions to control error propagation in language model generation.
• It combines MCMC principles with optimized rejection sampling to ensure convergence to a stationary distribution and maintain syntactic and semantic accuracy.
• Empirical evaluations on tasks such as Dyck grammar and code generation demonstrate VGB’s effectiveness in outperforming traditional methods and reducing error compounding.

Taming Imperfect Process Verifiers: Benefits of Stochastic Backtracking

Introduction to Stochastic Backtracking

This essay examines a novel approach called VGB (Value-Guided Backtracking) for mitigating errors in LLM generation due to imperfect process verifiers. Process verifiers assess the quality of LLM generations, and errors often amplify across long sequences, causing performance deterioration. The heart of VGB's strategy is its stochastic backtracking mechanism, which allows the system to probabilistically revisit earlier decisions during generation, drawing parallels to the Sinclair-Jerrum random walk used in approximate sampling.

The VGB algorithm interprets language generation as a sequence of decisions or actions, modeled as a tree where paths represent possible generations. Backtracking introduces the ability to probabilistically invalidate and revise previous steps, an idea grounded in theoretical guarantees of Markov chain Monte Carlo (MCMC) methods.

Detailed Implementation of VGB

Algorithm Description:

The VGB algorithm modifies the autoregressive sampling method by incorporating a backtracking probability. Each generation step considers revisiting prior decisions proportionally weighted by the verifier's valuation and the model's base output probabilities. This change forms a Markov chain with stationary distribution closely approximating the target distribution even when the verifier is imperfect.

Rejection Sampling Efficiency:

For large action spaces, VGB employs a rejection sampling mechanism optimized to handle varied action spaces efficiently, ensuring its applicability to both small-token and large-block generation tasks.

Theoretical Guarantees Under Uniform Error

VGB’s design ensures rapid convergence with uniform error bounds on the value function approximations:

• Stationary Distribution: The algorithm naturally finds its way to a stationary distribution aligned with the target distribution by balancing forward sampling and backward revisions.
• Conductance and Mixing Time: By employing conductance analysis, the algorithm rapidly mixes to a degree that ensures with high probability, generated sequences are statistically indistinguishable from the target distribution without error compounding.

The detailed theoretical assessment of VGB's performance, particularly under uniform error bounds, showcases its resilience. This uniformity implies that errors in the value function do not accumulate, contrasting sharply with traditional methods where such errors propagate unchecked.

Empirical Evidence from Synthetic and Real Tasks

Real-world applications affirm the theoretical findings via diverse task evaluations:

• Dyck Grammar Task: This task illustrates VGB’s prowess in managing structured outputs where syntactical balance is pivotal. It consistently outperforms traditional methods along the accuracy-diversity Pareto frontier.
• Code Generation: By leveraging its backtracking facility, VGB demonstrates superior distributional accuracy in generating syntactically valid code examples without direct access to ground truth within the verification phase.

These applications highlight VGB's adaptability to other constrained text generation problems, such as producing content with specific syntactic or semantic constraints.

Implications for Future Developments in AI

VGB’s introduction of stochastic backtracking presents not only a practical tool for today's computational linguistics challenges but also paves the way for exploring fundamentally new territory in algorithmic design and its theoretical underpinnings. The convergence of sampling and decision-making strategies reflects a broader trend in AI, seeking robust and efficient methods to safely navigate errors in complex models, thus fostering next-generation systems capable of more reliable and nuanced reasoning.

Conclusion

The VGB algorithm represents a significant step toward addressing error compounding in LLM generation through innovative use of stochastic backtracking. Its balance between theoretical rigor and practical efficiency offers promising avenues for future research and deployment in AI systems challenged by incomplete or imperfect process verifiers. By ensuring the robustness of generated outputs despite verifier inaccuracies, VGB stands at the forefront of advancing language generation methodologies.