SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

• URL: http://arxiv.org/abs/2604.08477v1
• Date: Thu, 09 Apr 2026 17:16:07 GMT
• Title: SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
• Abstract summary: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathematics and code.<n> Extending RLVR to general reasoning is constrained by the lack of high-quality, verifiable training data that spans diverse reasoning skills.<n>We propose SUPERNOVA, a data curation framework for RLVR aimed at enhancing general reasoning.
• Score: 17.62959060143886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathematics and code. Despite these advancements, LLMs still struggle with general reasoning tasks requiring capabilities such as causal inference and temporal understanding. Extending RLVR to general reasoning is fundamentally constrained by the lack of high-quality, verifiable training data that spans diverse reasoning skills. To address this challenge, we propose SUPERNOVA, a data curation framework for RLVR aimed at enhancing general reasoning. Our key insight is that instruction-tuning datasets containing expert-annotated ground-truth encode rich reasoning patterns that can be systematically adapted for RLVR. To study this, we conduct 100+ controlled RL experiments to analyze how data design choices impact downstream reasoning performance. In particular, we investigate three key factors: (i) source task selection, (ii) task mixing strategies, and (iii) synthetic interventions for improving data quality. Our analysis reveals that source task selection is non-trivial and has a significant impact on downstream reasoning performance. Moreover, selecting tasks based on their performance for individual target tasks outperforms strategies based on overall average performance. Finally, models trained on SUPERNOVA outperform strong baselines (e.g., Qwen3.5) on challenging reasoning benchmarks including BBEH, Zebralogic, and MMLU-Pro. In particular, training on SUPERNOVA yields relative improvements of up to 52.8\% on BBEH across model sizes, demonstrating the effectiveness of principled data curation for RLVR. Our findings provide practical insights for curating human-annotated resources to extend RLVR to general reasoning. The code and data is available at https://github.com/asuvarna31/supernova.

Related papers

• LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards [51.45138356629732]
  We introduce LongRLVR to augment the sparse answer reward with a dense and verifiable context reward.<n>This auxiliary signal directly incentivizes the model for selecting the correct grounding information.<n>LongRLVR consistently and significantly outperforms the standard RLVR across all models and benchmarks.
  arXiv  Detail & Related papers  (2026-03-02T18:07:53Z)
• Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning [82.91265691530351]
  A$2$D is an Adaptive Ability Decomposing method for enhancing the effectiveness ofReinforcement Learning with verifiable rewards.<n>We first train a decomposer via RLVR without distillation, enabling it to decompose complex questions into a set of simpler sub-questions.<n>Next, we use this decomposer to annotate sub-questions for each question in the training dataset, and then train the reasoner under RLVR with sub-question guidance.
  arXiv  Detail & Related papers  (2026-01-31T14:48:23Z)
• Generalization of RLVR Using Causal Reasoning as a Testbed [20.97376329817835]
  Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for post-training large language models.<n>This paper provides an empirical study of RLVR generalization in the setting of probabilistic inference over causal models.
  arXiv  Detail & Related papers  (2025-12-23T20:45:31Z)
• Tailored Primitive Initialization is the Secret Key to Reinforcement Learning [61.29280885291581]
  Reinforcement learning (RL) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs)<n>We argue that initializing LLMs with diverse, high-quality reasoning primitives is essential for achieving stable and sample-efficient RL training.<n>We propose Tailor, a finetuning pipeline that automatically discovers and curates novel reasoning primitives.
  arXiv  Detail & Related papers  (2025-11-16T03:12:40Z)
• Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration [33.02780998281276]
  Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning ability of large language models.<n>This study investigates how simply leveraging intrinsic data properties, almost free benefit during training, can improve data efficiency for RLVR.
  arXiv  Detail & Related papers  (2025-11-02T04:16:47Z)
• Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning [3.437656066916039]
  Reinforcement with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities.<n>We investigate RLVR on two problems with fully verifiable solutions.<n>We find that RLVR improves evaluation metrics but often by reinforcing superficial Learning metrics rather than acquiring new reasoning strategies.
  arXiv  Detail & Related papers  (2025-10-30T23:16:02Z)
• Demystifying Reinforcement Learning in Agentic Reasoning [90.3737088727791]
  We conduct a comprehensive and systematic investigation to demystify reinforcement learning in agentic reasoning.<n>We highlight our key insights: (i) replacing stitched synthetic trajectories with real end-to-end tool-use trajectories yields a far stronger SFT.<n> Exploration-friendly techniques are crucial for agentic RL, such as clip higher, overlong reward shaping, and maintaining adequate policy entropy could improve the training efficiency.
  arXiv  Detail & Related papers  (2025-10-13T17:57:15Z)
• Reinforcement Learning on Pre-Training Data [55.570379963147424]
  We introduce Reinforcement Learning on Pre-Training data (R), a new training-time scaling paradigm for optimizing large language models (LLMs)<n>R enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL)<n>Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of R.
  arXiv  Detail & Related papers  (2025-09-23T17:10:40Z)
• Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning [93.00629872970364]
  Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks.<n>We introduce SPARKLE, a fine-grained analytic framework to dissect the effects of RL across three key dimensions.<n>We study whether difficult problems -- those yielding no RL signals and mixed-quality reasoning traces -- can still be effectively used for training.
  arXiv  Detail & Related papers  (2025-06-05T07:53:59Z)
• The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning [8.36595587335589]
  Visual Reinforcement Learning methods often require extensive amounts of data.<n>Model-based RL (MBRL) offers a potential solution with efficient data utilization through planning.<n>MBRL lacks generalization capabilities for real-world tasks.
  arXiv  Detail & Related papers  (2024-11-15T13:21:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.