RDPO applies magnitude-aware quantile normalization and Mahalanobis whitening to decorrelate heterogeneous rewards in multi-objective RL, improving instruction following and writing quality on LongCat-Flash post-training while staying competitive on reasoning and coding.
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Writingbench: A comprehensive benchmark for generative writing
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representative citing papers
LitVISTA benchmark shows frontier LLMs fail to jointly capture narrative function and structure in literary texts, with errors dominated by anchor identification.
A mutual evaluation system for LLMs that uses game-theoretic aggregation of peer reviews and validates alignment with human voting on subjective outputs.
Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.
SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.
HarDBench demonstrates that current LLMs are highly susceptible to draft-based jailbreak attacks for harmful content in co-authoring scenarios, and a safety-utility balanced alignment via preference optimization significantly reduces such outputs without harming benign performance.
LongWriter-Zero applies RL from a base model with specialized rewards for length, quality, and structure to outperform SFT baselines and larger models on long-writing benchmarks.
DeepResearch Bench supplies 100 expert-crafted PhD-level tasks and two human-aligned evaluation frameworks to measure deep research agents on report quality and citation accuracy.
EngGPT2MoE-16B-A3B matches or exceeds other Italian open-source LLMs on most international benchmarks while remaining competitive on ITALIC, though it trails some top international models.
MindDR combines a Planning Agent, DeepSearch Agent, and Report Agent with SFT cold-start, Search-RL, Report-RL, and preference alignment to reach competitive scores on research benchmarks using 30B-scale models.
Pith review generated a malformed one-line summary.
citing papers explorer
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Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization
RDPO applies magnitude-aware quantile normalization and Mahalanobis whitening to decorrelate heterogeneous rewards in multi-objective RL, improving instruction following and writing quality on LongCat-Flash post-training while staying competitive on reasoning and coding.
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LitVISTA: A Benchmark for Narrative Orchestration in Literary Text
LitVISTA benchmark shows frontier LLMs fail to jointly capture narrative function and structure in literary texts, with errors dominated by anchor identification.
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LLMs Judge Themselves: A Game-Theoretic Framework for Human-Aligned Evaluation
A mutual evaluation system for LLMs that uses game-theoretic aggregation of peer reviews and validates alignment with human voting on subjective outputs.
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Reward Hacking in Rubric-Based Reinforcement Learning
Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.
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SEIF: Self-Evolving Reinforcement Learning for Instruction Following
SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.
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HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing
HarDBench demonstrates that current LLMs are highly susceptible to draft-based jailbreak attacks for harmful content in co-authoring scenarios, and a safety-utility balanced alignment via preference optimization significantly reduces such outputs without harming benign performance.
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LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning
LongWriter-Zero applies RL from a base model with specialized rewards for length, quality, and structure to outperform SFT baselines and larger models on long-writing benchmarks.
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DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
DeepResearch Bench supplies 100 expert-crafted PhD-level tasks and two human-aligned evaluation frameworks to measure deep research agents on report quality and citation accuracy.
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Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs
EngGPT2MoE-16B-A3B matches or exceeds other Italian open-source LLMs on most international benchmarks while remaining competitive on ITALIC, though it trails some top international models.
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Mind DeepResearch Technical Report
MindDR combines a Planning Agent, DeepSearch Agent, and Report Agent with SFT cold-start, Search-RL, Report-RL, and preference alignment to reach competitive scores on research benchmarks using 30B-scale models.
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Qwen3 Technical Report
Pith review generated a malformed one-line summary.
- SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding