Token Gradient Cancellation Key to Stable Intra-Group Sequence Reward Learning
Token gradient exchangeability and cancellation conditions stabilize intra-group RL, resolving drift and collapse missed in standard RLHF analyses and linking directly to LLM alignment limitations.
Lede: Maintaining gradient exchangeability across token updates is a necessary condition for intra-group objectives to enable cancellation on weak-credit high-frequency tokens and prevent reward-irrelevant drift in sparse-reward RL for reasoning models (Zeng et al., arXiv:2604.13088).
The paper identifies ineffective update accumulation, solution probability drift and entropy collapse as direct consequences of disrupted exchangeability in dominant intra-group comparison methods; two common mechanisms render non-cancellation structural. This aligns with documented long-horizon instabilities in PPO-based RLHF (Schulman et al., arXiv:1707.06347) yet prior coverage of sequence-level rewards overlooked the token-level credit assignment mechanics that drive these patterns.
Synthesizing with process-versus-outcome supervision results (Lightman et al., arXiv:2305.20050) and offline preference optimization (Rafailov et al., arXiv:2305.18290), the analysis reveals how standard RLHF pipelines implicitly assume cancellation that intra-group setups routinely violate, explaining persistent alignment limitations in credit assignment for reasoning chains that earlier works missed. The proposed minimal transformations restore approximate cancellation in shared token space.
Empirical gains in stability, sample efficiency and final performance confirm the design condition, directly addressing core token gradient dynamics that underpin both capabilities and failure modes in current LLM alignment pipelines.
AXIOM: Token gradient cancellation prevents drift in sequence-level RL by enforcing exchangeability; adopting these minimal transformations could resolve core instability and credit-assignment failures that currently limit RLHF scaling and reliable LLM alignment.
Sources (3)
- [1]Primary Source(https://arxiv.org/abs/2604.13088)
- [2]Proximal Policy Optimization(https://arxiv.org/abs/1707.06347)
- [3]Let's Verify Step by Step(https://arxiv.org/abs/2305.20050)