Enhancing Vision-Language Model Training with Reinforcement Learning in
  Synthetic Worlds for Real-World Success
Authors: name: George Bredis, name: Stanislav Dereka, name: Viacheslav Sinii, name: Ruslan Rakhimov, name: Daniil Gavrilov
Organization: T-Tech

Interactive multimodal agents must convert raw visual observations into
coherent sequences of language-conditioned actions -- a capability that current
vision-language models (VLMs) still lack. Earlier reinforcement-learning (RL)
efforts could, in principle, endow VLMs with such skills, but they have seldom
tested whether the learned behaviours generalize beyond their training
simulators, and they depend either on brittle hyperparameter tuning or on
dense-reward environments with low state variability. We introduce
Vision-Language Decoupled Actor-Critic (VL-DAC), a lightweight,
hyperparameter-free RL algorithm. VL-DAC applies PPO updates to action tokens
while learning value only at the environment-step level: an arrangement, to our
knowledge, not previously explored for large VLMs or LLMs. This simple
decoupling removes unstable weighting terms and yields faster, more reliable
convergence. Training a single VLM with VL-DAC in one inexpensive simulator at
a time (MiniWorld, Gym-Cards, ALFWorld, or WebShop) already produces policies
that generalize widely: +50\% relative on BALROG (game-centric agentic
control), +5\% relative on the hardest part of VSI-Bench (spatial planning),
and +2\% on VisualWebBench (web navigation), all without degrading general
image understanding accuracy. These results provide the first evidence that a
simple RL algorithm can train VLMs entirely in cheap synthetic worlds while
delivering measurable gains on real-image agentic, spatial-reasoning, and
web-navigation benchmarks.