# A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning

arXiv: https://arxiv.org/abs/2608.08158 · HTML: https://mlsanity.com/paper/2608.08158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 97 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 17 of 17)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling](https://mlsanity.com/paper/2402.09345/) (2024)
- [Vision-Language Models as a Source of Rewards](https://mlsanity.com/paper/2312.09187/) (2023)
- [Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity](https://mlsanity.com/paper/2210.09579/) (2022)
- [Training Language Models with Language Feedback](https://mlsanity.com/paper/2204.14146/) (2022)
- [Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications](https://mlsanity.com/paper/2408.10215/) (2024)
- [Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning](https://mlsanity.com/paper/2104.06411/) (2021)
- [Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/2405.15194/) (2024)
- [Improving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/2502.01307/) (2025)
- [Adaptive Reward Design for Reinforcement Learning](https://mlsanity.com/paper/2412.10917/) (2024)
- [Useful Policy Invariant Shaping from Arbitrary Advice](https://mlsanity.com/paper/2011.01297/) (2020)
- [Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning](https://mlsanity.com/paper/2503.15724/) (2025)
- [Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards](https://mlsanity.com/paper/2410.12197/) (2024)
- [Offline Reinforcement Learning with Imputed Rewards](https://mlsanity.com/paper/2407.10839/) (2024)
- [Automating Potential-based Reward Shaping with Vision Language Model Guidance](https://mlsanity.com/paper/2606.27180/) (2026)
- [SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning](https://mlsanity.com/paper/2602.03201/) (2026)

## Abstract (arXiv)

Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning. Reward shaping addresses these limitations by supplementing the task reward with an auxiliary signal that can accelerate learning while, in the classical setting, the original objective remains the evaluation criterion. Established theory guarantees safety for fixed shaping signals: potential-based reward shaping preserves optimal policies when the auxiliary term is the discounted difference of a time-invariant potential. In contemporary reinforcement learning systems, however, both the learner and the information available for guidance evolve during training: value estimates improve, novelty diminishes, feedback shifts, and predictive models are refined. Adaptive reward mechanisms occur across exploration, Bayesian inference, human-in-the-loop learning, automated reward design, and foundation-model-based approaches. This study introduces a unified analytical framework for comparing dynamic reward shaping and neighbouring adaptive reward mechanisms. The proposed framework distinguishes parametric revision from state-dependent variation, separates additive shaping from reward replacement and reward-adjacent guidance, and organises existing methods along temporal, informational, and theoretical dimensions. Using this framework, twelve method families are comparatively analysed. The framework further highlights the conditions under which optimality guarantees survive contemporary deep reinforcement learning pipelines, replay buffers, bootstrapped critics, and reward normalisation, while exposing the unresolved relationship between adaptation rate and learner stability.