Recipes for Steering and Scaling LLMs via Sampling
Introduces a flexible, theoretically grounded framework for steering and scaling autoregressive LLMs via sampling-based probabilistic inference Proposes two algorithms—Sequential Monte Carlo (SMC) and Replica Exchange (RE)—that steer generation toward powering, product, or tilting of the base model distribution Demonstrates unsupervised generation quality scaling without external reward models or supervision Shows superior scaling compared to Best-of-N and standard MCMC baselines Provides a syst
Analysis
TL;DR
- Introduces a flexible, theoretically grounded framework for steering and scaling autoregressive LLMs via sampling-based probabilistic inference
- Proposes two algorithms—Sequential Monte Carlo (SMC) and Replica Exchange (RE)—that steer generation toward powering, product, or tilting of the base model distribution
- Demonstrates unsupervised generation quality scaling without external reward models or supervision
- Shows superior scaling compared to Best-of-N and standard MCMC baselines
- Provides a systematic recipe for probabilistic inference with LLMs via sampling
Why It Matters
This work addresses a critical gap in LLM inference: moving beyond simple greedy or top-k sampling toward principled probabilistic methods that can steer model outputs without relying on external reward models or human supervision. For practitioners, it offers practical algorithms that improve generation quality at scale, while for researchers, it opens new directions in sampling-based inference for autoregressive models.
Technical Details
- Framework: A unified framework for steering LLM distributions through sampling, targeting powered, product, or tilted variants of the base autoregressive model distribution
- SMC Algorithm: Uses Sequential Monte Carlo to approximate target distributions by sequentially resampling and reweighting candidate generations, enabling efficient exploration of the output space
- Replica Exchange (RE) Algorithm: Applies parallel tempering across multiple replicas at different temperature scales, allowing the method to escape local modes and better explore complex distribution landscapes
- No External Supervision: Both methods operate without reward models or labeled data, relying purely on the base model's own probability distribution modified through mathematical transformations
- Benchmarking: Evaluated against Best-of-N and standard MCMC baselines, demonstrating more favorable scaling properties as computational budget increases
Industry Insight
- Sampling-based inference methods like SMC and RE could become standard tools for improving LLM output quality without the cost and complexity of training separate reward models
- The unsupervised nature of these techniques makes them particularly valuable for domains where labeled data or reward signals are scarce or expensive to obtain
- As LLMs grow larger, the favorable scaling of these methods suggests they may offer better cost-quality tradeoffs than brute-force Best-of-N approaches for production deployment
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