Learning to Generalize from Sparse and Underspecified Rewards
Proposes Meta Reward Learning (MeRL) combining KL divergence strategies to improve exploration and generalization in sparse reward settings for semantic parsing.
Key Findings
Methodology
This paper introduces a framework combining mode covering KL divergence for diverse trajectory collection and mode seeking KL divergence for targeted policy refinement. The core is Meta Reward Learning (MeRL), which employs gradient-based meta-learning (MAML) to optimize auxiliary reward functions, enhancing policy generalization. Additionally, Bayesian Optimization is used as a gradient-free alternative for reward tuning. Experiments on weakly supervised semantic parsing datasets (WikiTableQuestions, WikiSQL) demonstrate that the approach effectively balances exploration and exploitation, leading to significant performance gains. Key components include: β’ Using mode covering KL divergence to sample diverse successful trajectories; β’ Applying mode seeking KL divergence to focus on promising trajectories; β’ Meta-learning auxiliary reward functions to improve validation performance; β’ Bayesian Optimization for efficient reward parameter tuning.
Key Results
- On WikiTableQuestions, MeRL achieves a 1.2% accuracy improvement over baselines; on WikiSQL, a 2.4% increase. The method enhances trajectory diversity and robustness against sparse rewards. Ablation studies show that mode covering exploration significantly increases the number of successful trajectories, while auxiliary reward optimization boosts generalization. Bayesian Optimization offers comparable results with lower computational cost, validating the effectiveness of combined exploration and reward tuning strategies.
- In-depth analysis reveals that auxiliary rewards guide policies away from spurious solutions, improving validation accuracy and reducing overfitting. The success trajectories become more diverse, and the model adapts better to unseen contexts. The experimental results confirm that integrating exploration strategies with meta-learned reward functions outperforms traditional reinforcement learning approaches in weak supervision scenarios.
- Ablation experiments highlight the importance of mode covering exploration in discovering multiple successful trajectories, which is crucial for environments with sparse feedback. The auxiliary reward functions, learned via meta-learning or Bayesian optimization, effectively filter out accidental successes. Overall, the combined approach demonstrates superior performance and robustness, setting new state-of-the-art results in weakly supervised semantic parsing.
Significance
This work advances reinforcement learning by addressing the challenge of sparse, underspecified rewards, a common obstacle in real-world AI applications. By integrating exploration strategies with meta-learned reward functions, it significantly improves the ability of models to generalize from limited feedback. The approach reduces reliance on manual reward engineering, enabling more autonomous and scalable learning systems. Its success in semantic parsing indicates potential for broader applications such as program synthesis, robotics, and natural language understanding, where explicit reward signals are often unavailable or unreliable. This research paves the way for more robust, adaptable AI agents capable of learning in complex, real-world environments with minimal supervision.
Technical Contribution
The key technical innovations include: β’ A novel combination of mode covering and mode seeking KL divergence strategies to balance exploration and exploitation; β’ A meta-learning framework (MeRL) that optimizes auxiliary reward functions based on validation performance, promoting better generalization; β’ The application of Bayesian Optimization as a flexible, gradient-free method for reward tuning, suitable for non-differentiable objectives; β’ Demonstrating that these strategies significantly outperform existing reward learning methods like reward augmentation (RAML) and maximum marginal likelihood (MML) in weak supervision tasks. These contributions collectively enhance the robustness and efficiency of reinforcement learning in environments with sparse and ambiguous rewards.
Novelty
This research uniquely combines mode covering and mode seeking KL divergence strategies to regulate exploration and exploitation dynamically, a novel approach in reinforcement learning. The integration of meta-learning for auxiliary reward function optimization, along with Bayesian Optimization for hyperparameter tuning, represents a significant departure from prior reward shaping or inverse RL methods. Unlike previous works that rely heavily on manual reward engineering or demonstration data, this framework automatically learns reward functions that improve policy generalization. Its application to weakly supervised semantic parsing, achieving state-of-the-art results, underscores its novelty and practical impact, marking a new direction in reward design and exploration strategies.
Limitations
- Despite its strengths, the approach may struggle in environments with extremely sparse or deceptive rewards, where successful trajectories are exceedingly rare, limiting exploration effectiveness.
- The computational cost of meta-learning and Bayesian Optimization can be high, posing challenges for large-scale or real-time applications.
- Designing effective feature representations for auxiliary rewards remains a manual process, and poor feature choices could hinder performance or introduce bias.
- The current framework is primarily tested on semantic parsing; its generalization to other domains requires further validation.
Future Work
Future research will focus on automating feature extraction for auxiliary rewards, possibly via representation learning. Extending the framework to multi-task and continuous learning settings could further improve adaptability. Additionally, integrating multi-modal data (visual, auditory) and scaling to larger, more complex environments are promising directions. Improving computational efficiency, perhaps through approximation techniques or parallelization, will be crucial for real-world deployment. Exploring unsupervised or self-supervised reward signals could further reduce dependency on labeled data, broadening applicability.
AI Executive Summary
Reinforcement learning (RL) faces significant challenges when rewards are sparse and ambiguous, often leading to poor exploration and overfitting to accidental successes. Traditional methods struggle to discover diverse successful trajectories, especially in tasks like weakly supervised semantic parsing where explicit reward signals are limited. To address this, the paper introduces a novel framework combining mode covering and mode seeking KL divergence strategies, which balance exploration diversity and goal-focused learning. Central to this approach is Meta Reward Learning (MeRL), a meta-learning scheme that optimizes auxiliary reward functions based on validation performance, encouraging policies to focus on meaningful success signals. Additionally, Bayesian Optimization is employed as a flexible, gradient-free alternative for reward tuning, providing robustness and efficiency. Experiments on datasets such as WikiTableQuestions and WikiSQL demonstrate that the combined approach improves accuracy by 1.2% and 2.4%, respectively, surpassing existing state-of-the-art methods. The results highlight the importance of exploration diversity and reward refinement in environments with limited feedback. The proposed framework effectively filters out spurious trajectories, enhances generalization, and reduces reward engineering efforts. This work marks a significant step forward in making reinforcement learning more autonomous and scalable in real-world applications, especially in natural language understanding and program synthesis. Despite its success, challenges remain in scaling computationally intensive methods and automating feature design for auxiliary rewards, guiding future research toward more efficient, adaptable, and domain-agnostic solutions.
Deep Dive
Abstract
We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often underspecified: they do not distinguish between purposeful and accidental success. Generalization from underspecified rewards hinges on discounting spurious trajectories that attain accidental success, while learning from sparse feedback requires effective exploration. We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories, followed by a mode seeking KL divergence to train a robust policy. We propose Meta Reward Learning (MeRL) to construct an auxiliary reward function that provides more refined feedback for learning. The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy. The MeRL approach outperforms our alternative reward learning technique based on Bayesian Optimization, and achieves the state-of-the-art on weakly-supervised semantic parsing. It improves previous work by 1.2% and 2.4% on WikiTableQuestions and WikiSQL datasets respectively.