arXiv Machine Learning
MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces
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Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning
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arXiv Machine Learning
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EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning
arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.
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SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
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arXiv Machine Learning
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Understanding Enrichment in Reinforcement Learning
The paper investigates how enrichment—adding intermediate guidance to sparse rewards in reinforcement learning—affects policy-gradient updates. It shows that omitting or truncating importance-weight corrections implicitly reweights rewards, and decomposes the resulting gradient error into scale, rotation, and variance components. The authors propose a sequential Monte Carlo weight correction that mitigates variance growth, and apply their analysis to a fine-tuned Qwen3-1.7B model on OpenMathReasoning, illustrating how enrichment helps prevent collapse in sparse domains.
arXiv Machine Learning
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ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning
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