June 2026

Today, Sakana AI launched Sakana Fugu. It is a multi-agent orchestration system that behaves like one model. You send a request to a single endpoint. Fugu decides how to handle it internally. It solves a task directly when that is enough. It also assembles and coordinates a team of expert models when needed. The complexity of a multi-agent system never reaches your code. TL;DR Fugu delivers a multi-agent system behind one OpenAI-compatible API. Fugu Ultra leads most published […]

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In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task is completed, when a reward of +1 is given. Training a policy to maximize such a sparse reward requires solving a challenging credit assignment problem, leading to slow or ineffective RL improvement. We propose a simple approach to transform a sparse outcome reward into a dense process reward. […]

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Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs poorly. This approach incurs several downsides: it requires the robot to fail before data collection is triggered, provides little guidance about which states require supervision, and wastes demonstrator effort on redundant parts of the task where the policy already performs well. In this paper, we propose an active, continual learning paradigm for VLAs. We demonstrate that active, […]

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Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health. The Multi-Objective Personalized Interpretable Health-aware Food Recommendation System (MOPI-HFRS), from which this work extends, addresses this by jointly optimizing preference, health, and diversity through Pareto-based optimization. However, this approach relies on static, per-step tradeoff solutions that fail to capture the sequential nature of dietary decision-making. We introduce MORL-A2C, a sequential decision-making […]

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Previous work has found a gap between the scale of neural networks that reliably learn Conway’s Game of Life, and minimal networks capable of representing the classic cellular automaton with hard-coded parameter values. Viewing neural network learning as a search process suggests a dependence on networks large enough to contain sub-networks with lucky initializations (sometimes known as ‘winning tickets’) that actually learn the task. In this work, we reorient our perspective from discovering Life rules as a search […]

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Hi everyone, I am currently training an RL agent for an autonomous driving task, but I’ve hit a wall with Reward Engineering. Right now, I am stuck in a tedious, manual trial-and-error loop: The car stops completely to avoid risk -> I add a too_slow_penalty. The car then drives too aggressively at intersections -> I add an overspeed_penalty. As a result, my reward function is becoming bloated with too many heuristics and hyperparameters. Tuning one weight to fix […]

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