Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation
arXiv:2604.13088v2 Announce Type: replace Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) typically relies on sparse terminal rewards, which creates a poorly conditioned credit-assignment problem: the final feedback is propagated uniformly across all intermediate decisions. This leads to high gradient variance, unstable training, and many ineffective updates, ultimately limiting sustained model improvement. We propose a counterfactual-comparison framework for credit assignment. For each input, the framework samples multiple reasoning trajectories and
The rapid advancement of large language models necessitates more efficient and stable training methods to overcome current limitations in reinforcement learning credit assignment.
Improved credit assignment in LLMs can lead to more robust, capable, and economically viable AI agents, accelerating their deployment across various sectors.
This research proposes a new framework to mitigate high gradient variance and unstable training in LLMs, potentially unlocking more effective and sustained model improvement.
- · AI developers
- · Companies deploying LLM-based agents
- · AI research institutions
- · Inefficient LLM training methodologies
- · AI companies reliant on sparse reward systems
More efficient and stable training of large language models.
Faster development and deployment of more sophisticated AI agents capable of complex multi-step reasoning.
Enhanced automation and productivity gains across industries due to more reliable and adaptable AI systems.
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Read at arXiv cs.LG