Tiny Models, Big Reasoning Gains: Usc Researchers Introduce Tina For Cost-effective Reinforcement Learning With Lora

Achieving strong, multi-step reasoning successful LMs remains a awesome challenge, contempt notable advancement successful wide task performance. Such reasoning is important for analyzable problem-solving domains, specified arsenic technological investigation and strategical planning. Traditionally, enhancing reasoning skills involves supervised fine-tuning (SFT), wherever models study by imitating step-by-step reasoning demonstrations from much precocious models, specified arsenic o1. While effective, this method heavy depends connected nan readiness of high-quality reasoning traces, which are costly and consequence promoting shallow mimicry complete genuine logical exploration. RL offers an replacement by enabling models to study straight from reward signals, encouraging broader reasoning exploration. However, RL approaches are often resource-heavy and complex, raising nan mobility of really to build reasoning-capable models cost-effectively.

Following nan merchandise of beardown models for illustration o1-preview, respective open-source efforts specified arsenic STILL, Sky-T1, SimpleRL, PRIME, and DeepScaleR person explored businesslike strategies to replicate aliases surpass o1’s reasoning capabilities. Techniques see lightweight imitation learning, scalable instruction tuning, and simplified RL methods. Meanwhile, newer innovations, specified arsenic Group Relative Policy Optimization (GRPO), heighten RL training ratio by eliminating nan request for abstracted worth networks, arsenic seen successful models for illustration DeepSeek-R1. To further little training costs, researchers are besides investigating Low-Rank Adaptation (LoRA) methods, which update only a mini subset of exemplary parameters, maintaining modularity while preserving reasoning ability. This attack enables businesslike fine-tuning without nan computational demands of full-parameter updates.

Researchers from nan University of Southern California present Tina, a family of compact reasoning models that execute beardown capacity pinch minimal cost. Using RL enhanced by LoRA connected a 1.5B parameter guidelines model, Tina models outperform aliases lucifer state-of-the-art models astatine a fraction of nan computational expense. Their champion exemplary improves reasoning capacity by complete 20% and achieves 43.33% Pass@1 connected AIME24, pinch a post-training costs of conscionable $9. By leveraging LoRA’s ratio to accommodate reasoning formats while preserving guidelines knowledge, Tina highlights a highly accessible, cost-effective approach, pinch each resources afloat open-sourced.

Tina is simply a family of mini reasoning models built by post-training nan DeepSeek-R1-Distill-Qwen-1.5B exemplary utilizing LoRA during reinforcement learning pinch a GRPO-style approach. The model emphasizes minimalism—tiny models, mini parameter updates, and a debased hardware and fund footprint. Tina models were trained utilizing nationalist datasets and replicated setups from models for illustration STILL-3, DeepScaleR, and Open-RS. Training leveraged nan OpenR1 codebase, minimal hyperparameter tuning, and conscionable 2 NVIDIA L40S GPUs, occasionally RTX 6000 Ada GPUs. Training and information costs were low, averaging good nether a $100 fund per experiment, making Tina a highly accessible level for reasoning research.

To guarantee adjacent comparisons, nan authors reevaluated baseline reasoning models utilizing a accordant setup pinch nan LightEval model and vLLM engine, thereby eliminating variations introduced by erstwhile studies. Six reasoning benchmarks, including AIME 24/25, AMC 23, MATH 500, GPQA, and Minerva, were utilized. They past evaluated Tina models—small, LoRA-trained versions of baseline models—showing that Tina models often outperformed their full-parameter counterparts contempt utilizing minimal training (19–57% of an epoch). Further ablation studies revealed that smaller, high-quality datasets, due learning rates, mean LoRA ranks, and observant prime of RL algorithm importantly impacted performance, confirming nan ratio and robustness of their LoRA-based reasoning approach.

In conclusion, Tina, a bid of lightweight reasoning models that execute beardown capacity utilizing minimal computational resources. By applying LoRA during RL connected a 1.5 B-parameter guidelines model, they execute reasoning abilities competitory pinch larger state-of-the-art models astatine a post-training costs of conscionable $9. Tina models show complete a 20% betterment successful reasoning and 43.33% Pass@1 accuracy connected AIME24. While showcasing awesome cost-performance efficiency, limitations remain, including nan smaller exemplary scale, constricted diverseness successful reasoning tasks, and minimal hyperparameter tuning. All code, logs, and exemplary checkpoints are open-sourced to beforehand accessible investigation and further exploration.

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