Token-Efficient RL for LLM Reasoning
Alan Lee; Harry Tong · 2025 · arXiv
WASTE classifies this as Failed Experiment Report · AI classification, approximate
An experimental approach did not work as intended — learn what to avoid before investing the same effort.
Abstract (excerpt)
We propose reinforcement learning (RL) strategies tailored for reasoning in large language models (LLMs) under strict memory and compute limits, with a particular focus on compatibility with LoRA fine-tuning. Building on early policy gradient methods with baseline subtraction, we design critic-free methods that operate on a small, informative subset of output tokens to reduce memory usage and stabilize training. We introduce S-GRPO, a stochastic variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching approach for fine-grained credit assignment. Applied to Qwen2
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Metadata source: arXiv