Scaling Reinforcement Learning Beyond Math: Researchers From Nvidia Ai And Cmu Propose Nemotron-crossthink For Multi-domain Reasoning With Verifiable Reward Modeling

Large Language Models (LLMs) person demonstrated singular reasoning capabilities crossed divers tasks, pinch Reinforcement Learning (RL) serving arsenic a important system for refining their heavy reasoning abilities. While RL techniques person shown peculiar occurrence successful mathematical reasoning and coding domains pinch well-defined rules and verifiable correctness criteria, extending these approaches to broader reasoning contexts presents important challenges, including constricted training information and difficulties successful ensuring cross-domain generalisation.

Evolution of Reasoning successful LLMs

The improvement of Chain-of-Thought (CoT) methodology marked a important advancement successful LLM reasoning capabilities. CoT has demonstrated substantial improvements across mathematics, science, and programming domains by incorporating multi-step intermediate reasoning processes earlier reaching conclusions. This attack allows models to break down analyzable problems into manageable steps, mirroring quality problem-solving processes.

While mathematical reasoning has dominated caller investigation owed to its verifiable nature, nan description of RL training to divers domains remains mostly unexplored. Prior investigation useful propose that blending mathematical contented pinch different verifiable domains tin amended capacity connected wide reasoning benchmarks. However, systematic investigation into really non-mathematical reasoning data, specified arsenic ineligible analysis, societal science, aliases humanities interpretation, impacts RL training effectiveness still represents a important investigation gap.

Challenges successful Diversifying Reasoning Domains

Recent investigation has explored methods for diversifying RL training datasets, yet questions astir optimal data-blending strategies and nan comparative value of various sources stay unanswered. A basal situation successful applying RL to wide reasoning tasks is processing verifiable reward models for domains lacking deterministic solutions. Domain-specific reasoning processes—whether rule-based and symbolic successful mathematics aliases contextual and heuristic successful fields for illustration rule and history—require different cognitive approaches. In summation to that, mobility formats (open-ended versus multiple-choice) request chopped reasoning strategies, suggesting that incorporating divers reasoning domains could importantly heighten LLMs’ wide cognitive capabilities.

Nemotron-CrossThink: A Multi-Domain Approach

Researchers from NVIDIA, Carnegie Mellon University, and Boston University present Nemotron-CrossThink, representing a systematic model for incorporating multi-domain corpora into RL training to heighten cross-task generalisation. The methodology follows a broad pipeline that curates divers information sources, including synthetic information from CommonCrawl and open-source question-answer pairs crossed STEM, humanities, law, and societal sciences. By applying templated formats (MCQ/Open-Ended) to constrain reply spaces, filtering samples for verifiable rewards, and implementing strategical data-blending recipes, nan model enables effective self-learning done RL crossed divers reasoning domains.

Key Results and Innovations

Nemotron-CrossThink importantly enhances LLM reasoning capabilities by integrating multi-domain information pinch different mobility formats. Models trained pinch this attack show not only higher accuracy but besides move consequence strategies—generating concise answers for general-purpose questions while providing elaborate responses for mathematical problems—thereby optimising conclusion costs while maintaining task-specific precision.

The model addresses nan situation of verifiable rewards successful non-deterministic domains done templated information curation that limits reply abstraction diversity. It besides provides an businesslike filtering attack that ranks general-purpose reasoning information by complexity, showing that training pinch much challenging samples amplifies RL effect crossed each domains. These innovations person led to important capacity gains successful some mathematical benchmarks (MATH-500: +30.1%, AMC23: +27.5%) and non-mathematical tasks (MMLU-PRO: +12.8%, GPQA-DIAMOND: +11.3%).

Comprehensive Data Curation

Nemotron-CrossThink originates pinch meticulous information curation from aggregate sources to guarantee diversity. The training dataset combines synthetically generated information from CommonCrawl and publically disposable open-source QA datasets, encompassing some general-purpose reasoning and mathematical content. General-purpose reasoning information includes MMLU, Natural Reasoning, and synthesised QA pairs spanning STEM fields, economics, societal sciences, and humanities, while mathematical reasoning incorporates datasets for illustration MATH and Numina-Math alongside synthetically generated problems.

Template Application and Data Filtering

To reside nan situation of verifiable rewards successful non-mathematical domains, nan model applies circumstantial templates to building question-answer formats: Multiple Choice Questions (MCQ) and Open-Ended questions. This attack exposes nan exemplary to divers reply formats and reasoning pathways while limiting reply abstraction variability to alteration effective reward modeling. Rigorous filtering removes samples that are infeasible to measure pinch rule-based reward functions, discarding MCQs wherever correct answers aren’t among nan choices and open-ended responses exceeding 10 words.

Strategic Data Blending and Reinforcement Learning

Nemotron-CrossThink employs Group Relative Policy Optimisation (GRPO) for reinforcement learning, which improves ratio by estimating baselines from group scores alternatively than utilizing a abstracted professional model. The methodology investigates nan effect of divers information sources, mobility types, and information usefulness done six chopped blending recipes. This systematic attack enables elaborate study of really general-purpose reasoning information complements mathematical reasoning, yet producing much adaptable and generalizable connection models.

Technical Contributions

The investigation demonstrates respective cardinal method advances successful multi-domain reasoning done reinforcement learning:

1. Templated question-answer formats supply much unchangeable reward modeling, pinch unified open-ended mobility formats improving capacity by 1.21% complete mixed formats, and short-form reply templates outperforming long-form ones by 1.20%.
2. Strategic data-blending proves essential, pinch multi-domain corpora boosting mean reasoning accuracy by 1.61% compared to math-only training while reducing token usage by 28%.
3. Model-driven filtering techniques efficaciously prime challenging samples by removing those solvable by smaller models, yielding an further 2.15% accuracy summation for Qwen-2.5-32B.

These findings correspond important advancement successful processing LLMs pinch robust reasoning capabilities crossed divers domains, moving beyond nan accepted attraction connected mathematical reasoning to encompass nan afloat spectrum of quality knowledge and conclusion patterns.

Experiments and Results

Experimental results show that different datasets importantly effect exemplary capacity crossed reasoning benchmarks. NuminaMath produced nan highest wide average, outperforming nan baseline by 8.30%, pinch peculiar spot successful mathematical tasks while besides generalizing good crossed divers domains. Synthetic question-answering information improved capacity by astir 1.0%, showing beardown accuracy successful MMLU-PRO, AGIEVAL, and MATH-500 tasks, confirming that synthetically generated instruction-style information tin efficaciously generalize erstwhile aligned pinch benchmark distributions.

The Nemotron-CrossThink attack consistently outperformed nan guidelines exemplary crossed various blending strategies. The general-purpose reasoning blend (Bgpr↑) achieved nan highest wide average, exceeding OPEN-REASONER-ZERO by astir 5% connected mean and showing important gains connected reasoning-focused benchmarks (+12.82% connected MMLU-PRO, +15.12% connected AGIEVAL). Though Bonly_math performed somewhat amended connected strictly mathematical tasks, it lagged connected non-mathematical reasoning benchmarks, demonstrating Bgpr↑’s superior versatility done beardown cross-domain transfer.

Further study revealed that open-ended mobility formats (Bopen↑) yielded stronger results connected mathematical benchmarks than multiple-choice formats (Bmcq↑), suggesting alignment pinch nan inherently open-ended building of mathematical problems. Mathematical reasoning information showed transferability to system reasoning tasks, while general-purpose information proved little effective successful isolation. This counterintuitive uncovering confirms that optimal general-purpose reasoning capacity requires including mathematical problems successful training blends.

Conclusion

Nemotron-CrossThink introduces a scalable model that enhances LLM generalization done reinforcement learning pinch multi-domain corpora. By strategically blending divers reasoning information pinch a 2:1 ratio of general-purpose to mathematical content, nan attack achieves a singular 13.36% mean betterment complete baselines. The investigation demonstrates that information diversity, not simply volume, drives broader reasoning capabilities. Through difficulty-based filtering and thoughtful template design, Nemotron-CrossThink establishes a applicable methodology for processing much generalizable, efficient, and reliable LLMs that widen self-learning beyond mathematical reasoning.

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