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ARXIV:2604.02007 · LLM REASONING · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.02007LLM REASONINGSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALERafael Pardinas · Ehsan Kamalloo · David Vazquez · Alexandre Drouin · arXiv
An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces.
Opportunity summary
Pain An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces. However, their training recipes and domain mixtures are often not disclosed.
Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models. However, their training recipes and domain mixtures are often not disclosed.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Trained with a strict 16K-token output budget, Apriel-Reasoner generalizes to 32K tokens at inference and improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and…
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces.
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10.48550/arXiv.2604.02007An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces.
Abstract
Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models. However, their training recipes and domain mixtures are often not disclosed. Joint optimization across domains poses significant challenges: domains vary widely in rollout length, problem difficulty and sample efficiency. Further, models with long chain-of-thought traces increase inference cost and latency, making efficiency critical for practical deployment. We present Apriel-Reasoner, trained with a fully reproducible multi-domain RL post-training recipe on Apriel-Base, a 15B-parameter open-weight LLM, across five domains using public datasets: mathematics, code generation, instruction following, logical puzzles and function calling. We introduce an adaptive domain sampling mechanism that preserves target domain ratios despite heterogeneous rollout dynamics, and a difficulty-aware extension of the standard length penalty that, with no additional training overhead, encourages longer reasoning for difficult problems and shorter traces for easy ones. Trained with a strict 16K-token output budget, Apriel-Reasoner generalizes to 32K tokens at inference and improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and LiveCodeBench while producing 30-50% shorter reasoning traces. It matches strong open-weight models of similar size at lower token cost, thereby pushing the Pareto frontier of accuracy versus token budget.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
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Dimensions overall score 7.0
PROBLEM
An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces. However, their training recipes and domain mixtures are often not disclosed.
METHOD
Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models. However, their training recipes and domain mixtures are often not disclosed.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Trained with a strict 16K-token output budget, Apriel-Reasoner generalizes to 32K tokens at inference and improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and LiveCodeBench while producing 30-50% s...
WHY NOW
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
We introduce an adaptive domain sampling mechanism that preserves target domain ratios despite heterogeneous rollout dynamics
Directly stated in the abstract as a key methodological innovation
partial
a difficulty-aware extension of the standard length penalty that, with no additional training overhead, encourages longer reasoning for difficult problems and shorter traces for easy ones
Explicitly described in the abstract as a core technical innovation
partial
improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and LiveCodeBench
Directly stated in the abstract with specific benchmark names
partial
while producing 30-50% shorter reasoning traces
Specific numeric improvement directly stated in the abstract
partial
It matches strong open-weight models of similar size at lower token cost
Directly stated in the abstract as a performance claim
partial
Trained with a strict 16K-token output budget, Apriel-Reasoner generalizes to 32K tokens at inference
Specific numeric details directly stated in the abstract
partial
Joint optimization across domains poses significant challenges: domains vary widely in rollout length, problem difficulty and sample efficiency
Directly stated as a problem statement in the abstract
partial
models with long chain-of-thought traces increase inference cost and latency, making efficiency critical for practical deployment
Directly stated as motivation in the abstract, though not specific to Apriel-Reasoner
partial
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An LLM post-training method that significantly improves reasoning accuracy and efficiency across diverse tasks with shorter inference traces.
Segment
LLM Reasoning
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Commercial read
7.0/10 public viability
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proof status
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Technical feasibility
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Run minimal reproduction from the Build Passport prototype path.
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0 references, 0 sources, 33% evidence coverage.
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missing
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No public implementation surface observed.
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Classify regulatory flags before commercialization planning.
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