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[2510.01143] Generalized Parallel Scaling with Interdependent Generations
Summary
The paper presents a novel approach, Bridge, for parallel scaling in LLM inference that generates interdependent responses, enhancing accuracy and consistency with minimal additional parameters.
Why It Matters
This research addresses the limitations of current parallel generation methods in large language models (LLMs) by proposing a system that improves response quality through interdependence, potentially transforming how AI systems utilize computational resources and generate outputs.
Key Takeaways
- Bridge enhances parallel LLM inference by generating interdependent responses.
- Achieves accuracy improvements of up to 39% with minimal parameter increase.
- Compatible with various post-generation aggregation techniques.
- Utilizes holistic tensors for better resource allocation in AI models.
- Offers a scalable solution for generating high-quality responses.
Computer Science > Artificial Intelligence arXiv:2510.01143 (cs) [Submitted on 1 Oct 2025 (v1), last revised 16 Feb 2026 (this version, v3)] Title:Generalized Parallel Scaling with Interdependent Generations Authors:Harry Dong, David Brandfonbrener, Eryk Helenowski, Yun He, Mrinal Kumar, Han Fang, Yuejie Chi, Karthik Abinav Sankararaman View a PDF of the paper titled Generalized Parallel Scaling with Interdependent Generations, by Harry Dong and 7 other authors View PDF HTML (experimental) Abstract:Parallel LLM inference scaling involves sampling a set of $N>1$ responses for a single input prompt. However, these $N$ parallel responses tend to be generated independently from each other, partitioning compute resources and leaving potentially useful information in one generation untapped by others. This is in contrast to response length scaling where past computation is used in all future steps. For higher quality responses and response sets, we propose Bridge to generate interdependent responses in parallel by rethinking batched LLM hidden states as holistic tensors rather than independent slices. With only a small amount (2.8%-5.1%) of new parameters, Bridge improves the relative mean accuracy gains from reinforcement learning with verifiable rewards by up to 39% and boosts consistency of correct responses. Trained once, Bridge scales to any generation width, all with greater performance than independent generations, unlocking a more general mode of parallel scaling that ef...
