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Life sciences · Preprint
arXiv · September 6, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes OSOL, a method to mitigate multi-domain interference in RL-based LLM training by detecting and correcting cross-step output backtracking rather than relying only on same-step gradient diagnostics. The method improves performance on Qwen3-30B-A3B by 5.7% over baseline, but the work is unreviewed, tested on a single model, and lacks statistical rigor and generalization evidence.
Preprint. Qwen3-30B-A3B language model trained via GRPO (Group Relative Policy Optimization) on multiple domains. Intervention: OSOL: a cross-step control algorithm that designates a focus domain per iteration, ranks token-level rebound risk via preceding checkpoint footprint, and applies drift-ranked adaptive correction within GRPO update. Compared with: Unspecified baselines; text refers to 'strongest compared baseline' without naming them.
OSOL achieves domain-macro average of 0.4822 on Qwen3-30B-A3B, improving by 5.7% over the strongest compared baseline Cross-step backtracking is more strongly associated with subsequent task damage than same-point gradient diagnostics Preceding footprint ranks future rebound risk more accurately than Hessian-based proxies
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This is a methodological contribution from an unreviewed preprint proposing a novel algorithmic approach (OSOL) to a technical problem in multi-domain RL training, demonstrated on one model with controlled experiments but lacking peer review and clinical or real-world validation.
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Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often degrades individual-domain performance and can destabilize optimization. Existing work typically diagnoses such interference from a single-step view using first-order gradient alignment or curvature-based proxies. We show that this view can miss a critical form of sequential interference: same-point domain gradients may remain nearly orthogonal even when consecutive realized updates partially reverse one another in output space. We further show that consecutive token log-probability footprints recover this interaction directly from adjacent checkpoints as a local second-order interaction in output space, without explicitly reconstructing same-step curvature. Building on this insight, we propose OSOL, which designates a focus domain at each iteration, uses the preceding checkpoint footprint to rank token-level rebound risk, and applies a drift-ranked, adaptively scaled correction within the standard GRPO update. Our analysis shows that this correction suppresses the targeted cross-step output backtracking component. Controlled studies further show that cross-step backtracking is more strongly associated with subsequent task damage than same-point gradient diagnostics, while the preceding footprint ranks future rebound risk more accurately than Hessian-based proxies. On Qwen3-30B-A3B, OSOL reaches a domain-macro average of 0.4822, improving by 5.7% over the strongest compared baseline, without explicit higher-order differentiation.
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