CuSearch reallocates rollout budget in RLVR toward deeper-search trajectories as a proxy for retrieval supervision density, yielding up to 11.8 exact-match gains over uniform GRPO sampling on ZeroSearch.
Measuring and narrowing the compositionality gap in language models
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LEAD lets LLMs solve checkers jumping puzzles up to size 13 by using lookahead to recover from irreversible errors on hard steps that break extreme decomposition.
Derives an information-theoretic accuracy upper bound for single-pass LLM multi-hop QA and introduces the InfoQA multi-call framework that improves performance by keeping per-step information load within model capacity.
citing papers explorer
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CuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG
CuSearch reallocates rollout budget in RLVR toward deeper-search trajectories as a proxy for retrieval supervision density, yielding up to 11.8 exact-match gains over uniform GRPO sampling on ZeroSearch.
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LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning
LEAD lets LLMs solve checkers jumping puzzles up to size 13 by using lookahead to recover from irreversible errors on hard steps that break extreme decomposition.
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A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA
Derives an information-theoretic accuracy upper bound for single-pass LLM multi-hop QA and introduces the InfoQA multi-call framework that improves performance by keeping per-step information load within model capacity.