mlsanityData to 14 Oct 2026

GIFT: Global stabilisation via Intrinsic Fine Tuning

arXiv 2604.23312 ↗First posted 25 Apr 2026cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Deep reinforcement learning policies achieve strong performance in complex continuous control environments with nonlinear contact forces. However, these policies often produce chaotic state dynamics, with trivially small changes to the initial conditions significantly impacting the long-term behaviour of the control system. This high sensitivity to initial conditions limits the application of Deep RL to real-world control systems where performance and stability guarantees are often required. To address this issue, we propose Global stabilisation via Intrinsic Fine Tuning (GIFT), a general-purpose training framework which directly optimises the global stability of existing high-performing deep RL policies using a custom reward function. We demonstrate that GIFT increase the stability of the control interaction while maintaining comparable task performance, thereby improving the suitability of deep RL policies for real-world control systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 21 references that are in the map. First posted on arXiv 25 Apr 2026.

Builds on

9 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach14 Oct

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2023Mastering Diverse Domains through World Modelscited by 1,253
  5. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  6. 2020dm_control: Software and Tasks for Continuous Controlcited by 375
  7. 2020Neural Lyapunov Controlcited by 142
  8. 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 45

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Close in wording, no citation either way

  1. 2024Continuous Control with Coarse-to-fine Reinforcement Learning
  2. 2024Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies
  3. 2026SAFE: Stable Alignment Finetuning with Entropy-Aware Predictive Control for Reinforcement Learning from Human Feedback (RLHF)
  4. 2025MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning
  5. 2026Sufficiency of Zeroth-Order Reward Shaping for Policy Gradient in Stabilization Control
  6. 2025Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
  7. 2024Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions
  8. 2025Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems