MathNet delivers the largest multilingual Olympiad math dataset and benchmarks where models like Gemini-3.1-Pro reach 78% on solving but embedding models struggle on equivalent problem retrieval, with retrieval augmentation yielding up to 12% gains.
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Deepseekmath-v2: Towards self-verifiable mathematical reasoning
11 Pith papers cite this work. Polarity classification is still indexing.
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DataPRM is a new process reward model for data analysis agents that detects silent errors via environment interaction and ternary rewards, yielding 7-11% gains on benchmarks and further RL improvements.
DORA's multi-version streaming rollout enables 2-3x higher throughput in asynchronous RL for LLMs while preserving convergence by maintaining policy consistency, data integrity, and bounded staleness.
Introduces MemHome benchmark and RL with multi-dimensional rewards for memory-driven smart home device control.
ImpRIF improves LLM complex instruction following by synthesizing data from reasoning graphs and training models to reason explicitly along those graphs.
Pseudo-Formalization decomposes natural language proofs into modular blocks for independent LLM verification via Block Verification, outperforming LLM-as-judge baselines on error detection in olympiad and research math benchmarks.
STAR-PólyaMath introduces a multi-agent framework with meta-strategic supervision and state-machine orchestration that reports state-of-the-art and perfect scores on eight top math competition benchmarks.
A closed-loop system couples LLM-based 3D scene generation with RL optimization and VR user interactions to produce adaptive, immersive environments, claiming SOTA results on the ALFRED benchmark.
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
Lack of exploration from conditioning on prior answers is the primary reason parallel sampling outperforms sequential sampling in large reasoning models.
A six-dimensional MathVerifier supplies hard negatives and per-sample weights that improve DPO performance on math reasoning for a 1.5B Qwen2.5 model over standard SFT and unweighted DPO.
citing papers explorer
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MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval
MathNet delivers the largest multilingual Olympiad math dataset and benchmarks where models like Gemini-3.1-Pro reach 78% on solving but embedding models struggle on equivalent problem retrieval, with retrieval augmentation yielding up to 12% gains.
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Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
DataPRM is a new process reward model for data analysis agents that detects silent errors via environment interaction and ternary rewards, yielding 7-11% gains on benchmarks and further RL improvements.
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DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
DORA's multi-version streaming rollout enables 2-3x higher throughput in asynchronous RL for LLMs while preserving convergence by maintaining policy consistency, data integrity, and bounded staleness.
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Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards
Introduces MemHome benchmark and RL with multi-dimensional rewards for memory-driven smart home device control.
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ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
ImpRIF improves LLM complex instruction following by synthesizing data from reasoning graphs and training models to reason explicitly along those graphs.
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Pseudo-Formalization for Automatic Proof Verification
Pseudo-Formalization decomposes natural language proofs into modular blocks for independent LLM verification via Block Verification, outperforming LLM-as-judge baselines on error detection in olympiad and research math benchmarks.
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STAR-P\'olyaMath: Multi-Agent Reasoning under Persistent Meta-Strategic Supervision
STAR-PólyaMath introduces a multi-agent framework with meta-strategic supervision and state-machine orchestration that reports state-of-the-art and perfect scores on eight top math competition benchmarks.
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Closing the Loop: Unified 3D Scene Generation and Immersive Interaction via LLM-RL Coupling
A closed-loop system couples LLM-based 3D scene generation with RL optimization and VR user interactions to produce adaptive, immersive environments, claiming SOTA results on the ALFRED benchmark.
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Riemann-Bench: A Benchmark for Moonshot Mathematics
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
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Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models
Lack of exploration from conditioning on prior answers is the primary reason parallel sampling outperforms sequential sampling in large reasoning models.
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Hard Negative Sample-Augmented DPO Post-Training for Small Language Models
A six-dimensional MathVerifier supplies hard negatives and per-sample weights that improve DPO performance on math reasoning for a 1.5B Qwen2.5 model over standard SFT and unweighted DPO.