Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation
Summary: arXiv:2604.13088v1 Announce Type: cross
Abstract
In sparse termination rewards, intra-group comparisons have become the dominant paradigm for fine-tuning reasoning models via reinforcement learning. However, long-term training often leads to issues like ineffective update accumulation (learning tax), solution probability drift, and entropy collapse. This paper presents a necessary condition for algorithm design from a token-level credit assignment perspective: to prevent reward-irrelevant drift, intra-group objectives must maintain gradient exchangeability across token updates, enabling gradient cancellation on weak-credit/high-frequency tokens.
Introduction
The field of reinforcement learning has seen significant advancements, particularly in the context of sparse termination rewards. The ability to conduct intra-group comparisons has allowed for better fine-tuning of reasoning models. However, with long-term training, several issues arise that can hinder the effectiveness of these models.
Challenges in Reinforcement Learning
As training progresses, several challenges emerge:
- Ineffective Update Accumulation: This phenomenon, referred to as “learning tax,” occurs when the updates do not contribute effectively to the overall learning process.
- Solution Probability Drift: Over time, the probabilities associated with finding optimal solutions may drift, leading to suboptimal performance.
- Entropy Collapse: A reduction in the diversity of the model’s outputs can occur, limiting the model’s ability to explore new solutions.
Proposed Design Condition
This paper proposes a crucial design condition that addresses these challenges from a token-level credit assignment perspective. Specifically, to prevent reward-irrelevant drift, it is essential that intra-group objectives maintain gradient exchangeability across token updates. This condition enables gradient cancellation on weak-credit/high-frequency tokens, which is vital for stabilizing the learning process.
Mechanisms Disrupting Exchangeability
Our research identifies two common mechanisms that disrupt the exchangeability of gradients:
- Non-Cancellation Effects: These structural norms prevent effective gradient cancellation, leading to poor learning outcomes.
- Inadequate Token Transformations: Without appropriate adjustments to token transformations, the learning process can become unstable and inefficient.
Proposed Intra-Group Transformations
To address these disruptions, we propose minimal intra-group transformations aimed at restoring or approximating the cancellation structure within the shared token space. These transformations are designed to enhance the stability of the training process.
Experimental Results
The effectiveness of the proposed transformations was validated through a series of experiments. Results demonstrated significant improvements in:
- Stabilizing Training: The proposed design conditions helped maintain a steady learning trajectory.
- Improving Sample Efficiency: By optimizing the use of data, models were able to learn more effectively with fewer samples.
- Enhancing Final Performance: Overall, the modifications led to better end results, showcasing the advantage of implementing these design conditions.
Conclusion
This paper highlights the importance of maintaining gradient exchangeability in intra-group learning scenarios. By addressing the structural norms that lead to non-cancellation, we provide a pathway for more effective reinforcement learning models capable of overcoming common challenges associated with long-term training.