# Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning

arXiv: https://arxiv.org/abs/2412.11120 · HTML: https://mlsanity.com/paper/2412.11120/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods](https://mlsanity.com/paper/2404.00282/) (2024)
- [Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping](https://mlsanity.com/paper/2011.02669/) (2020)
- [Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance](https://mlsanity.com/paper/2310.10021/) (2023)
- [Reinforcement Learning with Trajectory Feedback](https://mlsanity.com/paper/2008.06036/) (2020)
- [History Compression via Language Models in Reinforcement Learning](https://mlsanity.com/paper/2205.12258/) (2022)
- [Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution](https://mlsanity.com/paper/2009.14108/) (2020)
- [Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics](https://mlsanity.com/paper/2309.06687/) (2023)
- [Learning Long-Term Reward Redistribution via Randomized Return Decomposition](https://mlsanity.com/paper/2111.13485/) (2021)
- [Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning](https://mlsanity.com/paper/1905.13420/) (2019)
- [Off-Policy Reinforcement Learning with Delayed Rewards](https://mlsanity.com/paper/2106.11854/) (2021)
- [Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach](https://mlsanity.com/paper/2305.18427/) (2023)
- [Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression](https://mlsanity.com/paper/2410.19400/) (2024)
- [Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning](https://mlsanity.com/paper/2309.01352/) (2023)
- [Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration](https://mlsanity.com/paper/2410.02511/) (2024)
- [Learning Retrospective Knowledge with Reverse Reinforcement Learning](https://mlsanity.com/paper/2007.06703/) (2020)
- [Episodic Return Decomposition by Difference of Implicitly Assigned Sub-Trajectory Reward](https://mlsanity.com/paper/2312.10642/) (2023)

## Built on by (most cited first, 23 of 23)

- [Outcome-based Reinforcement Learning to Predict the Future](https://mlsanity.com/paper/2505.17989/) (2025)
- [TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents](https://mlsanity.com/paper/2607.13988/) (2026)
- [Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments](https://mlsanity.com/paper/2504.19139/) (2025)
- [Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach](https://mlsanity.com/paper/2509.16736/) (2025)
- [Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization](https://mlsanity.com/paper/2502.14496/) (2025)
- [Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning](https://mlsanity.com/paper/2510.16877/) (2025)
- [QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2504.12961/) (2025)
- [ALTo: Adaptive-Length Tokenizer for Autoregressive Mask Generation](https://mlsanity.com/paper/2505.16495/) (2025)
- [HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving](https://mlsanity.com/paper/2505.15793/) (2025)
- [PROF: An LLM-based Reward Code Preference Optimization Framework for Offline Imitation Learning](https://mlsanity.com/paper/2511.13765/) (2025)
- [Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition](https://mlsanity.com/paper/2505.15922/) (2025)
- [Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.21828/) (2025)
- [Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2511.02567/) (2025)
- [KG-Augmented Executable CoT for Mathematical Coding](https://mlsanity.com/paper/2508.04072/) (2025)
- [The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping](https://mlsanity.com/paper/2604.11297/) (2026)
- [LinguaFluid: Language Guided Fluid Control via Semantic Rewards in Reinforcement Learning](https://mlsanity.com/paper/2508.05977/) (2025)
- [DecisionLLM: Large Language Models for Long Sequence Decision Exploration](https://mlsanity.com/paper/2601.10148/) (2026)
- [medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions](https://mlsanity.com/paper/2602.03305/) (2026)
- [Feedback-Normalized Developer Memory for Reinforcement-Learning Coding Agents: A Safety-Gated MCP Architecture](https://mlsanity.com/paper/2605.01567/) (2026)
- [Reward Shaping and Action Masking for Compositional Tasks using Behavior Trees and LLMs](https://mlsanity.com/paper/2605.05795/) (2026)
- [Correcting What You Cannot See: Credit Assignment for Perception Distillation in Multimodal Reasoners](https://mlsanity.com/paper/2607.28336/) (2026)
- [ProDVI: Programmatic Dynamics Priors for Value Network Initialization](https://mlsanity.com/paper/2608.06015/) (2026)
- [Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning](https://mlsanity.com/paper/2610.13504/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attributions stemming from overlooking the multifaceted nature of mission performance evaluation. Hopefully, Large Language Model (LLM) encompasses fruitful decision-making knowledge and provides a plausible tool for reward redistribution. Even so, deploying LLM in this case is non-trivial due to the misalignment between linguistic knowledge and the symbolic form requirement, together with inherent randomness and hallucinations in inference. To tackle these issues, we introduce LaRe, a novel LLM-empowered symbolic-based decision-making framework, to improve credit assignment. Key to LaRe is the concept of the Latent Reward, which works as a multi-dimensional performance evaluation, enabling more interpretable goal attainment from various perspectives and facilitating more effective reward redistribution. We examine that semantically generated code from LLM can bridge linguistic knowledge and symbolic latent rewards, as it is executable for symbolic objects. Meanwhile, we design latent reward self-verification to increase the stability and reliability of LLM inference. Theoretically, reward-irrelevant redundancy elimination in the latent reward benefits RL performance from more accurate reward estimation. Extensive experimental results witness that LaRe (i) achieves superior temporal credit assignment to SOTA methods, (ii) excels in allocating contributions among multiple agents, and (iii) outperforms policies trained with ground truth rewards for certain tasks.