Resample-Previous-Tokens (RPT)

Updated 1 July 2025

Resample-Previous-Tokens (RPT) is a set of machine learning techniques allowing sequential models, like language models, to iteratively revisit and correct previously generated tokens.
RPT significantly improves performance in tasks such as code generation and reasoning by reducing error accumulation, yielding approximately 10% relative accuracy gains on benchmarks like HumanEval+ and GSM8K.
This method integrates easily into existing autoregressive model pipelines with minimal architectural changes and low computational overhead, making it practical for augmenting pretrained models.

Resample-Previous-Tokens (RPT) encompasses a family of methodologies in modern machine learning that enable iterative correction or resampling of previously generated or encoded tokens during sequential modeling. RPT has been most prominently examined within the context of autoregressive LLM sampling, where it addresses the accumulation of irreversible errors—a recognized limitation of classical left-to-right generation. Recent research also explores RPT in sensorimotor modeling, relational data preparation, and reinforcement-based pretraining, each using the core idea of revisiting previously produced tokens for the sake of correction, efficiency, or improved representations.

1. Paradigm Shift in Autoregressive Sampling

Traditional autoregressive (AR) models generate tokens sequentially, conditioning each token $x_i$ only on the prefix $x_{<i}$ . This unidirectional sampling is "irrevocable," so early errors persist and can have cascading negative effects, particularly in structured tasks such as code completion or multi-step reasoning. RPT methods redefine this process by allowing the model to revisit and resample past tokens within a fixed window, conditioning not only on prior context but also on subsequent tokens in the window. The mathematical foundation of this approach generalizes the standard AR factorization,

$p(x) = \prod_{i=1}^n p(x_i \mid x_{<i})$

to a setting where,

$x_{i-\ell} \sim \hat{p}(x_{i-\ell} \mid x_{<i+1}, \overline{x_{i-\ell}})$

for $0\leq \ell < w$ , with $\overline{x_{i-\ell}}$ denoting the position to be resampled and $w$ the window size. This facilitates post-hoc correction based on available future context, enabling local self-correction and reducing the likelihood that errors in early tokens degrade the overall sequence quality (Gat et al., 6 Jun 2025 ).

2. Mechanism and Implementation in LLMs

RPT is applied by augmenting pretrained AR models through a fine-tuning process that introduces permutations or resampling within sliding windows of generated text. The architecture is preserved, but positional encodings are extended to signal permuted token orders. During training, for each batch:

Windows of tokens are randomly selected and permuted or swapped.
The model is required to predict the correct token values, now potentially conditioned on both prior and subsequent context within the window.
Cross-entropy loss is computed over targets reflecting either the canonical or permuted generation order.

The practical outcome is that the fine-tuned model can, at inference time, iteratively resample previously generated tokens, conditioned on their local context, usually in a sliding window fashion. The process may be repeated for a fixed number of iterations or until a confidence criterion is met (Gat et al., 6 Jun 2025 ).

3. Empirical Results and Performance Gains

Evaluations with fine-tuned 8B parameter LLMs demonstrate that RPT achieves substantial improvements over standard next-token prediction (NTP) sampling. On benchmarks such as GSM8K (reasoning) and HumanEval+, MBPP, MultiPL-E (coding), RPT yields approximately 10% relative improvement in accuracy. Specific results include increases from 25.6 to 28.6 on HumanEval+ and from 35.2 to 37.5 on GSM8K, with consistent reductions in total variation distance from the validation conditional distributions. RPT models also assign higher probability to ground truth tokens in a clear majority of validation instances.

Benchmark	Baseline	RPT k=1	RPT k=1.5
HumanEval+	25.6	27.4	28.6
MBPP	39.0	40.6	40.6
GSM8K	35.2	37.5	37.5
Java	37.9	41.1	41.1

RPT reduces the total variation distance between generated and gold distributions, indicating improved alignment with target distributions and more robust sample quality (Gat et al., 6 Jun 2025 ).

4. Integration and Compatibility

An important practical attribute of RPT is its ease of integration into existing AR pipelines. The method requires only minor changes to the training protocol, with no modification to underlying model architectures except for minimal positional encoding adjustments. RPT is compatible with key-value caching mechanisms and can be realized with negligible computational overhead relative to standard inference. Fine-tuning can be performed after the bulk of pretraining is completed, with effectiveness established using as little as 10% of full training compute (Gat et al., 6 Jun 2025 ).

5. Theoretical Rationale and Error Analysis

The core theoretical rationale for RPT is that error accumulation is reduced when the generative process is not strictly serial and "blind ahead." When the model can revise tokens using both left (history) and right (future context), empirical error rates for the revised tokens are demonstrably smaller than under purely left-to-right sampling. RPT utilizes conditionals for both next-token and previous-token prediction, the latter empirically incurring smaller errors and thus producing higher-quality, more coherent outputs.

Further, the comparative reduction in total variation distance and increase in likelihood assigned to correct tokens suggest that the resampling not only corrects errors but leads to more globally plausible sequence generation (Gat et al., 6 Jun 2025 ).

6. Related Approaches and Broader Context

RPT relates to, but is distinct from, methods such as beam search (which performs global search but offers no local correction once tokens are selected), blockwise parallel decoding, and diffusion-based denoising methods. Unlike these, RPT maintains compatibility with standard AR sampling, focusing on post-hoc, local correction with minimal inference and integration cost.

Selective LLMing (SLM) in Rho-1 (Lin et al., 11 Apr 2024 ) explores token-level selection during training, upweighting tokens that contribute most to downstream performance—a paradigm that could theoretically guide RPT’s choice of which tokens to resample for maximal benefit.

Beyond LLMing, analogous RPT-style ideas have appeared in:

Sensorimotor prediction for robotics, where RPT refers to self-supervised prediction of masked tokens (image, proprioceptive, and action) in sensorimotor sequences (Radosavovic et al., 2023 ).
Relational data preparation, where RPT denotes the iterative reconstruction of corrupted database tuples, but these uses are methodologically distinct and not related to AR token resampling (Tang et al., 2020 ).

7. Future Directions and Open Questions

Proposed extensions and open areas include:

Increasing the window size for more global corrections—early experiments did not yield further improvements, but new strategies for selecting tokens or blocks to resample may be beneficial.
Exploring non-local or block permutations, and integrating RPT with global search methods or diffusion generative processes for hybrid inference.
Systematic investigation of RPT’s behavior in more open-ended or creative generation, and its potential to synergize with token-level training selection methods for even greater efficiency and quality.
Addressing limitations or failure cases specific to RPT strategies, and developing theoretical understanding of when and how local resampling yields gains over traditional AR sampling (Gat et al., 6 Jun 2025 ).

Conclusion

Resample-Previous-Tokens (RPT) introduces an efficient, locally corrective sampling method for autoregressive models by iteratively resampling previously generated tokens within a sliding window, conditioned on both prior and subsequent context. Empirical evidence indicates that this approach significantly reduces error accumulation, improves reasoning and code generation benchmarks, and integrates seamlessly into existing AR model frameworks. RPT represents a practical shift in both the theory and software practice of sequence generation, opening further research into hybrid and more globally aware decoding strategies.

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