ARXIV:2604.08477 · AI AND MACHINE LEARNING · SUBMITTED 10 APR · 20:18 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

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

Q: What products could be built from this research?

The product could be a middleware API or a platform offering enhanced reasoning capabilities that other applications can leverage to provide smarter, more contextually aware decisions and suggestions across various industries.

Q: What are the practical use cases?

Develop a SaaS platform that integrates SUPERNOVA-enhanced LLM capabilities for companies needing advanced data analysis and decision support tools that require strong reasoning across diverse contexts.

Q: What industries could this research disrupt?

SUPERNOVA has the potential to disrupt traditional AI-powered reasoning systems by significantly improving the accuracy and reliability of general reasoning in LLMs, thus replacing outdated or less efficient models.

Ashima Suvarna · Kendrick Phan · Mehrab Beikzadeh · Hritik Bansal · Saadia Gabriel · arXiv

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Evidence 0 refs | 4 sources | 83% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks. Despite these advancements, LLMs still struggle with general reasoning tasks requiring capabilities such as causal inference and temporal…

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. The code and data is available at https://github.com/asuvarna31/supernova. A public repository is linked, so build verification can inspect implementation evidence instead of treating the…

WHY NOW

AI and Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainSUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Evidence0 refs | 4 sources | 83% coverage

Blockerno shell-level blocker reported

Analysis summary

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2604.08477 · AI AND MACHINE LEARNING · SUBMITTED 10 APR · 20:18 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

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

Ashima Suvarna · Kendrick Phan · Mehrab Beikzadeh · Hritik Bansal · Saadia Gabriel · arXiv

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Evidence 0 refs | 4 sources | 83% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

AI and Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainSUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Evidence0 refs | 4 sources | 83% coverage

Blockerno shell-level blocker reported

Analysis summary

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2604.08477

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

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Parse run linked

A document parse run is attached to this paper.

Proof status

unverified

0 refs; 4 sources; 83% coverage.

What was readable

linkedon file20 anchorsderived fallback38 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

coderepo url

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

AI and Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks. Despite these advancements, LLMs still struggle with general reasoning tasks requiring capabilities such as causal inference and temporal understanding.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The code and data is available at https://github.com/asuvarna31/supernova. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
AI and Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

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Methods

Materials

PDF linkedDocument parse run

Markets

AI and Machine Learning

Competitors

not indexed

Competitive landscape

SUPERNOVA enhances general reasoning in LLMs using reinforcement learning on curated instruction-based datasets, outperforming current benchmarks.

Segment

AI and Machine Learning

Adoption evidence

Public code linked for build inspection

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(38)

Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text

2026

HoneyBee: Data Recipes for Vision-Language Reasoners

2025

Does Learning Mathematical Problem-Solving Generalize to Broader Reasoning?

2025

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

2025

RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling

2025

OpenThoughts: Data Recipes for Reasoning Models

2025

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond

2025

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

2025

General-Reasoner: Advancing LLM Reasoning Across All Domains

2025

Qwen3 Technical Report

2025

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

2025

Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning

2025

OpenCodeReasoning: Advancing Data Distillation for Competitive Coding

2025

Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

2025

1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training

2025

SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

2025

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

2025

BIG-Bench Extra Hard

2025

LIMO: Less is More for Reasoning

2025

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

2025

Showing 20 of 38 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkGolden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text

7.0

Prior WorkGood Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning

7.0

Extension

Builds On ThisWhen Can LLMs Learn to Reason with Weak Supervision?

4.0

Builds On ThisNative Reasoning Models: Training Language Models to Reason on Unverifiable Data

6.0

Builds On ThisLearning from Synthetic Data Improves Multi-hop Reasoning

6.0

Builds On ThisDeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning

5.0

Builds On ThisLearning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes

5.0

Builds On ThisReSyn: Autonomously Scaling Synthetic Environments for Reasoning Models

5.0

Builds On ThisReasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks

3.0

Builds On ThisReinforced Efficient Reasoning via Semantically Diverse Exploration

6.0

Commercially relevant

none indexed

Conflicting

none indexed

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Run context

Paper: 2604.08477
Route: /paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions
Active tab: read
Artifact: supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

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API/MCP endpoints

REST paper pack API/api/v1/paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions/paper-pack
REST build passport API/api/v1/paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions/build-passport
REST OpenAPI/api/openapi.json
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MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-10
Score updated: 2026-04-10
Score fresh until: 2026-05-10
References: 0
Source count: 4
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

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

Canonical ID supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions | Route /paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

MCP example

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source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

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

/buildability/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

Build Nowready

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

Verdict

Build Now

Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

Paper ref

supernova-eliciting-general-reasoning-in-llms-with-reinforcement-learning-on-natural-instructions

arXiv id

2604.08477

Freshness

Generated at

2026-04-10T20:18:25.155Z

Evidence freshness

stale

Last verification

2026-04-10T20:18:25.155Z

Sources

References

Coverage

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Hash state

Lineage hash

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Canonical opportunity-kernel lineage hash.

Signature state

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Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: references

Pending verification refs / 4 sources / Verification pending

references

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Source Proof anchors

Visual citations from the paper document graph.

Source proof

Visual citation anchors from the paper document graph.

20 anchors

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This equation captures one of the core mathematical components of the system. i=1 from a behavior policy πθold and optimizes the GRPO (Shao et al., 2024) objective

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This equation captures one of the core mathematical components of the system. questions which are too easy for the model (win-rate=1) or too challenging (win-rate=0).

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JSON-LD twin

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

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

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