Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels (2510.06499v1)
Abstract: LLMs have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a training-generation gap and limits robust reasoning. Reinforcement learning (RL) offers a more data-efficient solution capable of bridging this gap, yet its application has been constrained by a critical data bottleneck: existing RL datasets are orders of magnitude smaller and less diverse than web-scale pre-training corpora. To address this, we introduce the Webscale-RL pipeline, a scalable data engine that systematically converts large-scale pre-training documents into millions of diverse, verifiable question-answer pairs for RL. Using this pipeline, we construct the Webscale-RL dataset, containing 1.2 million examples across more than 9 domains. Our experiments show that the model trained on this dataset significantly outperforms continual pretraining and strong data refinement baselines across a suite of benchmarks. Notably, RL training with our dataset proves substantially more efficient, achieving the performance of continual pre-training with up to 100$\times$ fewer tokens. Our work presents a viable path toward scaling RL to pre-training levels, enabling more capable and efficient LLMs.
Summary
- The paper presents a novel automated pipeline that scales RL data by converting pretraining corpora into verifiable QA pairs.
- It demonstrates superior data efficiency, achieving continual pretraining performance with up to 100× fewer tokens.
- Experimental results show significant gains in general knowledge and reasoning tasks compared to standard baselines.
Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels
Motivation and Problem Statement
The prevailing paradigm for training LLMs relies on imitation learning from massive web-scale corpora, typically via next-token prediction and supervised fine-tuning (SFT). This approach, while effective for knowledge acquisition, induces a training-inference gap due to teacher-forcing, resulting in models that are brittle under distribution shift and limited in robust reasoning. Reinforcement learning (RL) offers a principled alternative by optimizing models through online reward feedback, thereby narrowing the training-inference gap and improving data efficiency. However, RL's impact on LLMs has been fundamentally constrained by the scarcity and limited diversity of high-quality RL datasets, which are orders of magnitude smaller than pretraining corpora.
Figure 1: The RL data bottleneck: pretraining leverages >1T diverse tokens, while RL datasets are limited to <10B tokens. Webscale-RL converts pretraining corpora into verifiable QA pairs, scaling RL data to pretraining levels and improving efficiency.
Webscale-RL Data Pipeline
To address the RL data bottleneck, the paper introduces the Webscale-RL pipeline, an automated system for converting large-scale pretraining documents into diverse, verifiable question-answer (QA) pairs suitable for RL. The pipeline is designed to preserve the scale and diversity of web data, enabling RL training at a scale previously unattainable.
The pipeline consists of four main stages:
- Data Filtering: Heuristic and LLM-based filtering remove low-quality, non-informative, or non-self-contained documents.
- Domain Classification and Persona Assignment: Each document is classified into a domain (e.g., commerce, healthcare, STEM) and assigned multiple personas (e.g., medical expert, patient) to encourage diverse question perspectives.
- Verifiable QA Generation: Conditioned on the document, domain, and persona, an LLM generates self-contained, verifiable QA pairs, guided by few-shot domain-specific exemplars.
- Quality Check and Leakage Control: LLM-based verification ensures answer correctness and prevents information leakage (i.e., trivial answer extraction from the question).
Figure 2: Overview of the Webscale-RL pipeline, which converts pretraining data into RL-ready QA pairs using domain-specific demonstrations, persona assignment, and multi-stage verification.
This approach enables systematic, scalable conversion of pretraining corpora into RL data, with the potential to match pretraining scale and diversity.
Dataset Construction and Analysis
Applying the pipeline to a mixture of pretraining sources (DCLM, Wikipedia, MegaMath, Stack-v2, etc.), the authors construct the Webscale-RL dataset, comprising approximately 1.2 million QA pairs across more than nine domains. The dataset is designed for high scalability, as both questions and answers are grounded in pretraining documents, and the conversion process is automated.
Domain analysis reveals that Webscale-RL covers a broader range of topics than existing RL datasets, with significant representation in underrepresented domains such as lifestyle and commerce, in addition to STEM.
Figure 3: Left: Domain distribution of Webscale-RL. Right: UMAP visualization of question embeddings from Webscale-RL and Nemotron, showing broader coverage and diversity in Webscale-RL.
Embedding-based analysis (using Qwen3-Embedding and UMAP) demonstrates that Webscale-RL exhibits a more uniform and scattered distribution of question topics compared to Nemotron, indicating greater diversity and coverage.
Experimental Evaluation
Setup
- Model: Qwen2.5-3B, finetuned with GRPO on Webscale-RL.
- Baselines: Continual pretraining on original corpora, advanced data refinement (QuRating, ProX, GDR), all followed by SFT on 10K high-quality examples to mitigate instruction-following bias.
- Benchmarks: MMLU-pro, Big-Bench, GPQA-diamond, MATH500, GSM8K, MBPP, EvalPlus.
Main Results
Webscale-RL RL training outperforms all baselines across most benchmarks, with an average improvement of 3.4 points over the strongest data refinement baseline. Notably, RL with Webscale-RL achieves the performance of continual pretraining with up to 100× fewer tokens, demonstrating substantial data efficiency. Gains are most pronounced on general knowledge and reasoning tasks (MMLU-pro, Big-Bench, GPQA-diamond), with strong improvements in math (MATH500) and competitive results in coding, despite lower coding data representation.
Scaling Efficiency
A scaling analysis compares RL training on Webscale-RL with continual pretraining at varying token budgets. RL training consistently outperforms continual pretraining at all scales and achieves comparable performance with two orders of magnitude fewer tokens.
Figure 4: Scaling comparison: RL training on Webscale-RL outperforms continual pretraining at all scales and achieves similar performance with 100× fewer tokens.
This result highlights the superior scaling efficiency of RL when supplied with large, diverse, verifiable data.
Implementation Considerations
- Pipeline Automation: The entire data pipeline is automated using LLMs for filtering, classification, QA generation, and verification, enabling scalable dataset construction.
- Reward Model: RL training uses a binary reward based on exact answer match, which is stable but incurs additional inference cost for reward computation.
- Domain Balancing: The current dataset is limited in certain domains (e.g., coding); future work should rebalance pretraining sources to target specific applications.
- Resource Requirements: RL training with large-scale data is computationally intensive, but the improved data efficiency can offset training costs relative to continual pretraining.
Implications and Future Directions
The Webscale-RL pipeline demonstrates that RL data can be scaled to pretraining levels by systematic conversion of web-scale corpora, unlocking new performance and efficiency gains for LLMs. This approach decouples RL data growth from human annotation and teacher model distillation, enabling broader domain coverage and diversity.
Key implications:
- RL can be made a first-class training paradigm for LLMs, not just a post-training refinement step.
- Data efficiency gains suggest that smaller models can achieve competitive performance with sufficient RL data.
- The pipeline provides a template for constructing domain-targeted RL datasets by adjusting pretraining source composition.
Future work should address domain balancing (especially for code), explore more efficient reward models to reduce inference cost, and extend the approach to larger models and more complex reward structures.
Conclusion
Webscale-RL provides a scalable, automated solution to the RL data bottleneck in LLM training. By converting pretraining corpora into diverse, verifiable QA pairs, the pipeline enables RL training at unprecedented scale and diversity, resulting in improved model performance and data efficiency across a wide range of tasks. This work establishes a practical path for integrating RL as a core component of LLM training and sets the stage for further advances in scalable, robust, and general-purpose LLMs.
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Overview
This paper is about teaching AI LLMs in a better way. Today, most models learn by copying patterns from huge amounts of text on the internet. That works well, but it can make them weak at real problem-solving. The authors propose using reinforcement learning (RL)—a way of learning by trying, checking the result, and getting feedback. They build a new, automated data pipeline called Webscale-RL that turns large web text into millions of clear question-and-answer pairs so models can learn with RL at a much bigger scale.
Key Objectives
The paper aims to:
- Create a fast, automated way to turn web text into many reliable, short-answer questions.
- Build a large and diverse dataset for RL (1.2 million examples from 9+ different areas).
- Test whether training with RL on this dataset makes models better and more efficient than just continuing to read more text.
How They Did It (Methods)
Think of two ways to learn:
- Imitation learning (pretraining): like reading textbooks and copying the teacher’s writing style. It’s good for memorizing, but you might freeze when asked to solve a new problem.
- Reinforcement learning (RL): like practicing with quizzes, checking if your final answer is right, and learning from that feedback. This helps you get better at solving problems, not just copying text.
The challenge: RL needs lots of short, checkable questions with correct answers. That kind of data is rare compared to the massive amount of web text.
Their solution: an automated “data factory” that turns web pages into RL-ready questions. It works in four steps:
- Data filtering: remove messy pages (like menus or footers) and keep only informative, self-contained text.
- Domain tagging and persona assignment: label each document by topic (like health, science, commerce), and assign “personas” (like a doctor, a student, or a customer) so questions come from different viewpoints.
- QA generation: use an AI to write questions and short answers rooted in the document. The question includes enough context so it’s solvable without looking at the original page.
- Quality and leakage checks: verify the answer is correct according to the source, and make sure the question doesn’t give away the answer directly.
Result: a big RL dataset with 1.2 million verified question–answer pairs across many topics.
Main Findings
- Better performance: Models trained with RL on Webscale-RL beat models that just continue pretraining (even with fancy data-cleaning tricks) on many benchmarks, including general knowledge (MMLU-pro, Big-Bench), math (MATH500, GSM8K), and some coding tasks.
- More efficient: RL training reached the same performance as continued pretraining while using up to 100× fewer tokens. In simple terms, it learned more with less “reading.”
- Strong small models: A small model (3B parameters) trained with Webscale-RL moved closer to the performance of a much larger model (7B parameters), shrinking the gap noticeably.
Why this matters: It shows that practicing with checkable questions (RL) can be more powerful than just reading more text (imitation), especially for reasoning and problem-solving.
Why It Matters (Impact)
- Scaling RL to “web size”: This pipeline makes it possible to build huge, diverse RL datasets—similar in scale to the text used for pretraining. That could lead to smarter, more reliable LLMs.
- Better reasoning: Models trained with RL become more robust at solving problems because they learn by doing and checking, not just by copying patterns.
- Smaller, stronger models: RL can help smaller models catch up to bigger ones, which is useful for faster and cheaper AI.
- Future improvements: The dataset currently has less coding content, and the reward-checking step can be expensive. Next steps include adding more code data and designing faster reward systems.
In Short
The paper shows a new way to turn massive web text into millions of clean, checkable questions for RL. Training with this data makes models better at reasoning, more efficient, and more practical—even at smaller sizes. It’s a promising path to building the next generation of capable AI assistants.
Knowledge Gaps
Unresolved Gaps, Limitations, and Open Questions
Below is a concise list of concrete gaps that remain unaddressed and that future work could act on:
- Quantify data quality at scale: no audited error rates (false positives/negatives) for answer correctness and leakage checks; report sampling-based precision/recall, inter-rater reliability (human audit), and confidence intervals for the LLM verifier.
- Sensitivity to label noise: no analysis of how incorrect QA labels affect RL stability/performance; paper robustness curves versus controlled noise injection and characterize failure modes.
- Ablations on pipeline components: missing causal attribution for each stage (LLM-based filtering, domain tagging, persona assignment, few-shot library); measure per-component contribution to final performance and data quality.
- Persona efficacy and bias: no evidence that persona assignment improves utility; quantify its impact, potential topical/stylistic biases, and whether it introduces systematic skew.
- Diversity measurement: diversity assessed via UMAP visuals and domain counts only; provide formal diversity metrics (e.g., topic entropy, type-token diversity, question-type coverage, answer-format distribution) and link diversity to performance gains.
- Reward design limitations: reliance on exact-match binary rewards can be brittle to paraphrases, normalization, and multiple valid answers; explore fuzzy/semantic matching, canonicalization, programmatic rewards (unit tests for code), and hybrid process+outcome rewards.
- Process supervision: no use of process rewards or verification of intermediate reasoning quality; investigate whether outcome-only reward leads to spurious shortcuts or degraded chain-of-thought quality.
- Reward hacking and gaming: no checks for adversarial behaviors (e.g., formatting exploits, answer-template biases); design diagnostics to detect and mitigate reward exploitation.
- Compute and cost accounting: token-efficiency claims exclude rollout tokens, reward evaluation tokens, and verifier/generator costs; report end-to-end compute (FLOPs), wall-clock, and $ cost per performance point for RL vs continual pretraining.
- Scalability bottlenecks: pipeline demonstrated at 1.2M QA pairs; provide throughput benchmarks, cost breakdown per million items, and identification of blocking stages when scaling to billions.
- Proprietary LLM dependence: generation/verification uses GPT-4.1/4.1-mini; assess reproducibility with open models, cross-generator/verifier variance, and quality degradation under weaker models.
- Domain coverage gaps: coding underrepresented; no principled rebalancing/curriculum; design domain-aware sampling strategies, difficulty calibration, and coverage goals tied to target applications.
- Multilinguality: pipeline and evaluations are English-centric; investigate multilingual conversion, cross-lingual generalization, and non-Latin scripts, plus compatibility of reward/verification across languages.
- Temporal validity: documents can be outdated; add timestamp-aware QA generation and time-aware verification to prevent stale or contradicted answers.
- Decontamination scope: only lm-eval-harness overlap removal; add broader content-based deduplication (near-duplicate and paraphrase-level), and probe residual contamination on each benchmark.
- Evaluation breadth: no human evaluation, safety/toxicity/fairness audits, or calibration metrics; extend to multi-turn dialogue, tool-use, long-context reasoning, retrieval settings, and robustness (adversarial rephrasing, OOD shifts).
- Baseline fairness: no comparison to strong SFT at comparable scale on the same converted QA pairs; add SFT-only and DPO/KTO baselines on the identical question set to isolate RL’s contribution.
- RL dataset selection: RL training sampled 150K of 1.2M items; selection criteria, curriculum, difficulty stratification, and domain balancing not specified; paper sampling strategies and their impact.
- Generalization across model sizes: experiments limited to a 3B model; test scalability to larger models (7B, 14B, 70B) and cross-architecture transfer to assess external validity.
- Online RL vs offline QA: pipeline provides fixed, self-contained QA without interactive environments; evaluate whether this actually narrows the training–inference gap (e.g., exposure-bias diagnostics, interactive tasks).
- Answer normalization: no standardized normalization for dates, numerics, units, names; implement canonicalizers to reduce false negatives and quantify improvements.
- Code evaluation: coding tasks use exact-match reward rather than unit tests; integrate test-based rewards and measure delta vs exact-match.
- Safety and privacy: no PII/copyright/license auditing of converted QA; add automated PII detection, licensing filters, and safety red-teaming; report residual rates post-filtering.
- Data duplication and redundancy: no report on intra-dataset dedup of QA pairs; measure duplicate/near-duplicate rates and the effect of dedup on RL gains.
- Long-context reasoning: questions are constrained to be self-contained; paper whether this biases toward extractive comprehension and limits long-horizon reasoning and multi-hop retrieval generalization.
- Causal link to “bridging the gap”: claim that RL narrows training–generation gap is not directly tested; add targeted evaluations (e.g., free-running generation vs teacher-forcing discrepancy, exposure bias metrics).
- Clarify “generative reward model”: ambiguous description of a “generative reward model that provides binary feedback based on match”; specify implementation, calibration, and failure cases.
- Tokenization and curriculum effects: no analysis of token length distributions of questions/answers and their impact on learning; paper curriculum by length/difficulty and adaptive sampling.
- Robustness and uncertainty: no tests for calibration, abstention, or uncertainty estimates; measure calibration error and selective prediction under RL training.
- Downstream transfer: evaluate whether gains transfer to practical assistant tasks (planning, tool orchestration, retrieval-augmented QA) beyond static benchmarks.
- Open-source completeness: although code/dataset are released, exact prompts, few-shot libraries, and domain/persona taxonomies may affect reproducibility; ensure full artifacts and versioned dependencies are available and report their effect sizes.
Practical Applications
Below is a concise synthesis of practical, real-world applications that follow directly from the paper’s findings, methods, and innovations. The lists are grouped by deployment horizon and include sector tags, potential tools/workflows, and key assumptions or dependencies that affect feasibility.
Immediate Applications
These can be deployed with the provided code, dataset, and current LLM/RL tooling.
- Webscale-RL data engine for RL-ready corpora conversion (AI/Software)
- Use case: AI teams convert existing pretraining corpora or internal documents into large, diverse, verifiable QA datasets for RL training to improve model reasoning while cutting token budgets.
- Tools/workflows: https://github.com/SalesforceAIResearch/PretrainRL-pipeline, https://huggingface.co/datasets/Salesforce/Webscale-RL; LLM-based filtering/classification, persona assignment, QA generation, quality check; GRPO RL training; lm-eval-harness.
- Assumptions/dependencies: Access to high-quality corpora; LLMs for generation/verification; compute for RL and reward inference; data governance for internal document use.
- Cost-efficient uplift of small models via RL (AI/Software, Mobile/Edge)
- Use case: Startups and teams fine-tune 2–7B models with RL on Webscale-RL-style data to achieve stronger reasoning using up to 100× fewer tokens than continual pretraining.
- Tools/workflows: GRPO pipeline, small SFT warmup + RL; persona-diverse QA datasets; token budgeting; on-device deployment.
- Assumptions/dependencies: Efficient reward execution; baseline model quality; robust inference stack for RL training.
- Domain-specific RL datasets from proprietary sources (Healthcare, Finance, Legal, Commerce)
- Use case: Convert manuals, regulations, research summaries, customer policies, and reports into verifiable QA pairs to train assistants that answer precisely (numbers, dates, names, short factual phrases).
- Tools/workflows: Domain classification + persona assignment (e.g., “clinician,” “patient,” “regulator,” “analyst”); quality verification with leakage checks; RL finetuning.
- Assumptions/dependencies: Document rights and confidentiality; strong domain-specific few-shot exemplars; careful scope (not clinical/legal decision-making without oversight).
- Enterprise knowledge-base QA with verifiable answers (Customer Support/IT)
- Use case: Transform product manuals, SOPs, and help-center articles into verifiable QAs; train RL assistants to give precise, self-contained answers for support and troubleshooting.
- Tools/workflows: Source ingestion; persona selection (novice, expert, field technician); short-answer reward model; continuous dataset refresh.
- Assumptions/dependencies: Document freshness; evaluation decontamination; maintaining persona library quality.
- EdTech question bank generation and RL tutors (Education)
- Use case: Create large-scale, persona-aware question banks from textbooks and lecture notes; train RL tutors that follow instructions and provide correct, concise answers.
- Tools/workflows: Domain demos; personas (student, instructor, exam proctor); short-answer verification; SFT warmup + RL; classroom content pipelines.
- Assumptions/dependencies: Textbook licensing; alignment with curricula; quality thresholds in verification.
- Safety/compliance QA and leakage-resistant prompts (Policy/Compliance, Platform Safety)
- Use case: Build datasets that enforce verifiable answers and detect prompt leakage to reduce hallucinations and inadvertent disclosure in regulated environments.
- Tools/workflows: Leakage checks in quality verification; compliance corpora conversion; auditable QA generation logs.
- Assumptions/dependencies: Customized leakage policies; sector-specific compliance rules; human review for sensitive topics.
- Retrieval and search evaluation/training data (Software/Search)
- Use case: Use self-contained, verifiable QAs to train and evaluate retrievers and QA systems, improving exact-answer metrics across diverse domains.
- Tools/workflows: Self-contained questions with embedded context; embedding analysis (e.g., Qwen3-Embedding) for diversity checks; RAG + RL workflows.
- Assumptions/dependencies: High-quality context generation; domain-balanced corpora; careful benchmark decontamination.
- Data curation and refinement workflows for LLM training (AI/Software)
- Use case: Replace or complement standard data “cleaning” with pipeline-generated RL tasks that bridge the training-inference gap.
- Tools/workflows: Automated filtering, persona-driven generation, correctness and leakage verification; SFT warmup + RL.
- Assumptions/dependencies: Reliable LLM filters; domain demonstration libraries; reward stability in RL.
- Citizen-facing government information assistants (Public Sector)
- Use case: Convert agency FAQs, policies, and service guides into verifiable QAs to train assistants that provide precise, self-contained answers to citizens.
- Tools/workflows: Public corpus ingestion; personas (citizen, caseworker, journalist); quality checks to prevent leakage; RL training.
- Assumptions/dependencies: Accessibility and public data licenses; rigorous content review; transparent provenance.
- Benchmarking and method research using open code/data (Academia)
- Use case: Study RL scaling laws, reward shaping, persona effects, and domain diversity impacts using reproducible pipelines and public datasets.
- Tools/workflows: GitHub pipeline; Hugging Face dataset; lm-eval-harness; model ablations for data efficiency.
- Assumptions/dependencies: Compute resources; consistent evaluation methodology; contamination control.
Long-Term Applications
These require further research, scaling, domain-specific integration, or infrastructure development.
- Pretraining-level RL at trillion-token scale (AI/Software)
- Use case: Train general reasoning models with RL across the full diversity of web corpora, matching pretraining scale to reduce the training–inference gap.
- Tools/workflows: End-to-end web-crawl → conversion → verification → RL; distributed reward infrastructure; improved reward models beyond binary.
- Assumptions/dependencies: Efficient, low-cost reward modeling; scalable RL infrastructure; rigorous data governance and safety.
- Process-based and multi-step reward extensions (AI/Software, Code)
- Use case: Move beyond short-answer verification to process rewards for math, code, scientific reasoning, and multi-hop QA.
- Tools/workflows: Process reward models; programmatic verification (unit tests, formal proofs); chain-of-thought audits.
- Assumptions/dependencies: Reliable automatic validators; teacher models or structured tools; increased computational cost.
- Continuous knowledge refresh pipelines (AI/Software, Public Sector)
- Use case: Keep assistants up-to-date by automatically converting newly crawled or updated documents into RL datasets on a rolling basis.
- Tools/workflows: Scheduled ingestion; domain/persona updates; continuous RL; drift monitoring; safety filters.
- Assumptions/dependencies: Stable crawling; content licensing; compliance with data removal/consent policies.
- Clinical and legal decision-support with human-in-the-loop (Healthcare, Legal)
- Use case: Train high-reliability assistants for summarization and precise answers grounded in clinical guidelines or case law, with expert oversight.
- Tools/workflows: Domain-specific personas (clinician, patient, judge, counsel); layered verification; escalation policies; audit trails.
- Assumptions/dependencies: Regulatory approvals; rigorous validation pipelines; conservative deployment contexts.
- Tool-use and agentic RL training (Robotics, DevOps, Finance Ops)
- Use case: Integrate QA with actions (APIs/tools) and reward on task success to train agents that plan, retrieve, execute, and verify.
- Tools/workflows: RAG + tools + RL; environment simulators; success-based rewards; persona-driven task generation.
- Assumptions/dependencies: Stable tool APIs; sandboxed environments; safety constraints for execution.
- Sector-wide standards for synthetic RL data (Policy/Standards, Academia)
- Use case: Develop guidelines for verifiable QA construction, leakage checks, decontamination, and auditing to normalize high-quality RL datasets.
- Tools/workflows: Standards bodies and consortia; dataset governance metadata; transparent reporting.
- Assumptions/dependencies: Multi-stakeholder coordination; alignment across vendors; legal/ethical frameworks.
- Expanded benchmarks in underrepresented domains (Academia, Industry)
- Use case: Build RL benchmarks beyond math/code to lifestyle, commerce, healthcare communications, and social sciences.
- Tools/workflows: Domain rebalance strategies; persona expansion; standardized evaluation sets and protocols.
- Assumptions/dependencies: Broad domain corpora; open licensing; community adoption.
- Privacy-preserving on-device continual RL (Mobile/Edge, Consumer)
- Use case: Personal assistants fine-tune locally on user documents or notes, with privacy-preserving rewards and federated aggregation.
- Tools/workflows: Federated RL; local verification; synthetic short-answer tasks from user content; differential privacy.
- Assumptions/dependencies: Efficient on-device RL; strong privacy guarantees; careful user consent and data control.
- Industrial safety and operations assistants (Energy/Manufacturing/Transportation)
- Use case: Convert SOPs and safety manuals into verifiable RL tasks for operational support and training in risk-sensitive environments.
- Tools/workflows: Persona libraries (operator, inspector, safety officer); scenario-based verification; escalation procedures.
- Assumptions/dependencies: Safety certification; rigorous testing; domain expert involvement.
- Financial analysis and compliance assistants (Finance)
- Use case: Train assistants that provide verified numeric and regulatory answers; extend to reasoning with structured data, risk models, and simulations under RL.
- Tools/workflows: Data pipelines integrating filings, policies, and market data; structured validators; audit logs.
- Assumptions/dependencies: Data licensing; compliance constraints; robust validation for high-stakes outputs.
Notes on cross-cutting assumptions and dependencies:
- Reward model cost and stability are current bottlenecks; research into more efficient and expressive reward models will broaden applicability.
- Quality and coverage depend on domain-balanced corpora and high-quality few-shot demonstration libraries.
- For sensitive sectors (healthcare, legal, finance), human oversight, conservative deployment, and regulatory compliance are essential.
- Data governance (provenance, licensing, decontamination, leakage control, privacy) must be integral to any production pipeline.
Glossary
- Binary reward: An RL reward design that gives 1 for a correct outcome and 0 otherwise. "In our setup, we adopt a binary reward that returns $1$ only when the model's final answer matches the ground-truth answer and $0$ otherwise."
- Chain-of-Thought (CoT) prompting: A prompting technique that elicits step-by-step reasoning in LLMs to improve problem solving. "LLMs trained to reason with Chain-of-Thought (CoT) prompting have shown substantial performance gains in diverse areas,"
- Continual pretraining: Further pretraining a model on additional unlabeled data after initial pretraining. "outperforms continual pretraining and strong data refinement baselines"
- Data bottleneck: A limitation where insufficient or hard-to-obtain data restricts progress or scalability. "its application has been constrained by a critical data bottleneck"
- Data decontamination: The process of removing overlaps between training and evaluation sets to avoid leakage. "we further apply data decontamination by lm-eval-harness~\cite{eval-harness}"
- Data refinement: Techniques for improving raw training data quality before learning. "data refinement baselines"
- Deduplication: Removing duplicate entries from datasets to reduce redundancy and bias. "filtering and deduplicating publicly available web data sources"
- Distillation: Transferring knowledge from a stronger “teacher” model to a “student” model, often to generate labels or reasoning traces. "via distillation"
- Distribution shift: A mismatch between training and test (or deployment) data distributions that harms performance. "struggle with distribution shift"
- Domain-specific demonstration library: A curated set of in-domain examples used to guide few-shot generation. "domain-specific demonstration library for few-shot examples"
- Expected reward: The average reward a policy aims to maximize over its action distribution. "maximizes expected reward on a query"
- Few-shot examples: A small number of examples provided in the prompt to condition or guide generation. "few-shot examples"
- Generative reward model: A model that evaluates generated outputs to produce a reward signal for RL. "a generative reward model that provides binary feedback"
- Group Relative Policy Optimization (GRPO): An RL algorithm variant of PPO that normalizes rewards within groups to stabilize training. "Group Relative Policy Optimization (GRPO)~\cite{shao2024deepseekmath}"
- Leakage prevention: Measures ensuring that questions do not trivially reveal answers in the prompt. "Leakage prevention ensures that the questions do not reveal answers explicitly (e.g., the ground truth is not trivially embedded in the prompt)."
- lm-eval-harness: A standard evaluation toolkit for LLMs used for benchmarking and decontamination checks. "lm-eval-harness~\cite{eval-harness}"
- Negative log-likelihood: A loss function for next-token prediction that penalizes low probability assigned to observed tokens. "minimizing the negative log-likelihood:"
- Online learning: A training regime where the model updates based on feedback from its own generated outputs. "This online learning process makes RL a significantly more data-efficient training paradigm."
- Persona: A specified role or perspective used to diversify question generation styles and intents. "we assign multiple personas to each document"
- Policy (RL): The model’s conditional distribution over actions (outputs) given inputs, optimized to maximize reward. "optimizes the model as a policy that generates answers online"
- Proximal Policy Optimization (PPO): A widely used RL algorithm that constrains policy updates for stability. "Proximal Policy Optimization (PPO)~\cite{ppo}"
- ProX: A programmatic data-cleaning approach used to improve the quality of pretraining corpora. "ProX\cite{zhou2024programming}, which uses programmatic cleaning to enhance data quality"
- QuRating: A data selection method that ranks and filters examples via LLM-based judgments. "QuRating\cite{wettig2024qurating}, which selects high-quality data via LLM ranking and filtering"
- Reward function: The mapping from outputs (and possibly inputs) to scalar feedback used to guide RL optimization. "R is a task-specific reward function."
- Reward signal: The scalar feedback provided to guide learning during RL. "reduces the invalidity of the reward signal"
- Supervised fine-tuning (SFT): Training a model on labeled input–output pairs to align behavior with desired responses. "supervised fine-tuning (SFT)"
- Teacher forcing: Training with ground-truth next tokens provided at each step, potentially creating a mismatch at inference time. "``teacher-forcing''"
- Training-inference gap: The mismatch between the model’s training conditions and its generation-time conditions. "training-inference gap"
- UMAP: A dimensionality reduction technique used to visualize high-dimensional embeddings. "reduced to 2D using UMAP"
- Verifiable question-answer pairs: QA items whose answers can be unambiguously checked for correctness, enabling reliable RL rewards. "verifiable question-answer pairs for RL."
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