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ARXIV:2603.11193 · REINFORCEMENT LEARNING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.11193REINFORCEMENT LEARNINGSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training.
Opportunity summary
Pain DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and…
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
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DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training.
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10.48550/arXiv.2603.11193DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training.
Abstract
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these contexts remains underexplored. In this paper, we conduct controlled experiments revealing a critical challenge: for general STEM domains, RL applied directly to base models is highly sample-inefficient and is consistently surpassed by supervised fine-tuning (SFT) on moderate-quality responses. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is allocated between them matters. Therefore, we propose DeReason, a difficulty-based data decoupling strategy for general reasoning. DeReason partitions training data by reasoning intensity estimated via LLM-based scoring into reasoning-intensive and non-reasoning-intensive subsets. It allocates broad-coverage, non-reasoning-intensive problems to SFT to establish foundational domain knowledge, and reserves a focused subset of difficult problems for RL to cultivate complex reasoning. We demonstrate that this principled decoupling yields better performance than randomly splitting the data for sequential SFT and RL. Extensive experiments on general STEM and mathematical benchmarks demonstrate that our decoupled curriculum training significantly outperforms SFT-only, RL-only, and random-split baselines. Our work provides a systematic study of the interplay between SFT and RL for general reasoning, offering a highly effective and generalized post-training recipe.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
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Dimensions overall score 5.0
PROBLEM
DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these cont...
METHOD
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader general scientific (STEM) d...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is allocated between them matters.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these contexts remains underexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these contexts remains underexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is allocated between them matters.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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DeReason enhances reasoning in large language models through a difficulty-aware curriculum for training.
Segment
Reinforcement Learning
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Commercial read
5.0/10 public viability
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