arXiv AI
4d ago

Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening

The paper introduces Scalable Power Sampling, a training‑free method that sharpens a large language model’s generative distribution by approximating the autoregressive power distribution with a token‑level scaled low‑temperature distribution. This approach eliminates the need for iterative Markov chain Monte Carlo, achieving performance comparable to or better than one‑shot GRPO on math, QA, and code tasks while cutting inference latency by more than tenfold. The method relies solely on the base model’s outputs, requiring no external rewards or verifiers.

By Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer, Haitham Bou Ammar
arXiv Machine Learning
Sep 14

Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models

The paper introduces Decision-Flow Sampling (DF‑Sample), a training‑free, data‑free inference framework that builds a hierarchical reasoning tree, evaluates entire trajectories, and back‑propagates utilities to guide branching decisions. Unlike local step‑wise sampling, DF‑Sample explicitly assesses global paths, enabling it to recover high‑quality, low‑probability reasoning chains that standard decoding misses. On the GPQA benchmark, DF‑Sample attains 45.6% accuracy, outperforming power sampling (38.9%) and GRPO (39.9%) and consistently surpassing baselines across multiple models and benchmarks, demonstrating significant latent reasoning potential in pretrained LLMs.

By Zhendong Mi, Shaoyi Huang
arXiv AI
3d ago

Generative Recursive Reasoning

Generative Recursive Reasoning (GRAM) extends Recursive Reasoning Models by introducing probabilistic multi‑trajectory computation, allowing neural reasoning systems to explore multiple latent trajectories instead of a single deterministic path. GRAM treats reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and scalable inference through recursive depth and parallel sampling. Trained via amortized variational inference, GRAM outperforms deterministic recurrent and recursive baselines on structured reasoning and multi‑solution constraint satisfaction tasks, and also supports unconditional generation.

By Junyeob Baek, Mingyu Jo, Minsu Kim, Mengye Ren, Yoshua Bengio, Sungjin Ahn
arXiv Machine Learning
Sep 1

Delta-AI: Local objectives for amortized inference in sparse graphical models

arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\D...

By Jean-Pierre Falet, Hae Beom Lee, Esmeralda S. Whitammer, Chen Sun, Dragos Secrieru, Thomas Jiralerspong, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio