Bootstrapped Mixed Rewards for RL Post-Training: Injecting Canonical Action Order

A recent study published on arXiv presents a non-asymptotic convergence analysis of stochastic gradient Langevin dynamics (SGLD) in the lazy training regime, demonstrating that SGLD achieves exponential convergence to the empirical risk minimizer under certain conditions. The findings are supported by numerical examples in regression settings.

LongVT has been introduced as an innovative framework designed to enhance video reasoning capabilities in large multimodal models (LMMs) by facilitating a process known as 'Thinking with Long Videos.' This approach utilizes a global-to-local reasoning loop, allowing models to focus on specific video clips and retrieve relevant visual evidence, thereby addressing challenges associated with long-form video processing.

TempR1 has been introduced as a temporal-aware multi-task reinforcement learning framework designed to enhance the temporal understanding of Multimodal Large Language Models (MLLMs). This framework aims to improve capabilities in long-form video analysis, including tasks such as temporal localization and action detection.

The recent study on Group Relative Policy Optimization (GRPO) in Search-R1 highlights a significant issue known as Lazy Likelihood Displacement (LLD), which leads to a collapse in training effectiveness. This phenomenon results in a self-reinforcing cycle of declining response quality, characterized by low-confidence outputs and inflated gradients. The research empirically demonstrates this collapse across various models engaged in search-integrated question answering tasks.

A novel framework named LangSAT has been introduced, which integrates reinforcement learning (RL) with natural language processing (NLP) to enhance Boolean satisfiability (SAT) solving. This system allows users to input standard English descriptions, which are then converted into Conjunctive Normal Form (CNF) expressions for solving, thus improving accessibility and efficiency in SAT-solving processes.

A recent study published on arXiv investigates the use of generic masculines (GM) in contemporary German press texts, analyzing their distribution and linguistic characteristics. The research focuses on lexeme-specific differences among personal nouns, revealing significant variations, particularly between passive role nouns and prestige-related personal nouns, based on a corpus of 6,195 annotated tokens.

Recent research has uncovered a limit cycle organization in the articulatory movements that generate human speech, challenging the conventional view of speech as discrete actions. This study reveals that rhythmicity, often associated with acoustic energy and neuronal excitations, is also present in the motor activities involved in speech production.

Recent research highlights the limitations of hierarchical instruction schemes in large language models (LLMs), revealing that these models struggle with consistent instruction prioritization, even in simple cases. The study introduces a systematic evaluation framework to assess how effectively LLMs enforce these hierarchies, finding that the common separation of system and user prompts fails to create a reliable structure.