Balancing Accuracy And Efficiency In Language Models: A Two-phase Rl Post-training Approach For Concise Reasoning

Recent advancements successful LLMs person importantly enhanced their reasoning capabilities, peculiarly done RL-based fine-tuning. Initially trained pinch supervised learning for token prediction, these models acquisition RL post-training, exploring various reasoning paths to get astatine correct answers, akin to really an supplier navigates a game. This process leads to emergent behaviors specified arsenic self-correction, often called nan “aha moment,” wherever models statesman revising their mistakes without definitive instruction. While this improves accuracy, it besides results successful overmuch longer responses, expanding token usage, computational costs, and latency. Despite assumptions that longer outputs equate to amended reasoning, investigation shows mixed results—some improvements are seen, but excessively lengthy answers tin besides trim performance, indicating diminishing returns.

Researchers are exploring ways to equilibrium reasoning value and ratio to reside this. Methods see utilizing smaller, faster models, applying punctual engineering to trim verbosity, and processing reward-shaping techniques encouraging concise yet effective reasoning. One notable attack is long-to-short distillation, wherever models study from elaborate explanations and are trained to nutrient shorter yet meticulous answers. Using these techniques, models for illustration Kimi person demonstrated competitory capacity moreover against larger models for illustration GPT-4 while consuming less tokens. Studies besides item nan conception of “token complexity,” showing that problems require a minimum token period for meticulous resolution, and punctual strategies aimed astatine conciseness often autumn short of this optimal point. Overall, nan findings stress nan value of processing much businesslike reasoning methods without compromising performance.

Researchers from Wand AI situation nan belief that longer responses inherently lead to amended reasoning successful ample connection models. Through theoretical study and experiments, they show that this verbosity is simply a by-product of RL optimization alternatively than a necessity for accuracy. Interestingly, concise answers often correlate pinch higher correctness, and correct responses are shorter than incorrect ones. They propose a two-phase RL training approach: The first shape enhances reasoning ability, while nan 2nd enforces conciseness utilizing a mini dataset. This method reduces consequence magnitude without sacrificing accuracy, offering improved ratio and capacity pinch minimal computational cost.

Longer responses do not ever lead to amended capacity successful connection models. RL post-training tends to trim consequence magnitude while maintaining aliases improving accuracy, particularly early successful training. This counters nan belief that agelong reasoning chains are basal for correctness. The arena is tied to “deadends,” wherever excessively agelong outputs consequence veering off-course. Analyzing connection tasks arsenic Markov Decision Processes reveals that RL minimizes loss, not length, and longer outputs only originate erstwhile rewards are consistently negative. A two-phase RL strategy—first connected difficult problems, past connected solvable ones—can boost reasoning while yet promoting conciseness and robustness.

The two-phase RL strategy led to notable capacity gains crossed different exemplary sizes. Training connected varying trouble levels showed that easier problems helped models shorten responses while maintaining aliases improving accuracy. A 2nd RL shape utilizing conscionable 8 mathematics problems produced much concise and robust outputs crossed benchmarks for illustration AIME, AMC, and MATH-500, pinch akin trends seen successful STEM tasks from MMLU. Even minimal RL post-training improved accuracy and stableness nether low-temperature sampling. Furthermore, models without anterior RL refinement, specified arsenic Qwen-Math-v2.5, showed ample accuracy boosts—up to 30% from training connected only 4 mathematics problems.

In conclusion, nan study presents a two-phase RL post-training method that improves reasoning and conciseness successful connection models. The first shape enhances accuracy, while nan 2nd focuses connected shortening responses without sacrificing performance. Applied to R1 models, this attack reduced consequence magnitude by complete 40% while maintaining accuracy, particularly astatine debased temperatures. The findings uncover that longer answers are not inherently amended and that targeted RL tin execute concise reasoning. The study besides highlights that moreover minimal RL training tin greatly use non-reasoning models, emphasizing nan worth of including moderately solvable problems and cautiously tuning PPO parameters.

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