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Faynt: Scaling and Optimizing Policies for Competitive Melee

Paper recorded by Signals 4 on 2026-10-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.LG · 机器学习 · first seen 2026-10-02

Abstract

We introduce Faynt, a family of 10M- and 75M-parameter Transformer policies for Super Smash Bros. Melee, each controlling all 26 characters with a single checkpoint. After reinforcement learning (RL), the 10M wins 240 of 244 same-character games (98.4%) against fourteen specialist and multi-character releases on their supported rosters, with a winning record against every release. These opponents

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#13 most recent of 362 cs.LG papers we have recorded · ↑ newer: Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and · ↓ older: Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis
Cite this page: Faynt: Scaling and Optimizing Policies for Competitive Melee: the #13 most recent of 362 cs.LG papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/faynt-scaling-and-optimizing-policies-for-competitive-melee.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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