LongTraceRL trains LLMs on long-context reasoning by generating tiered-distractor data from search trajectories and using positive-only entity rubric rewards for process supervision.
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5verdicts
UNVERDICTED 5representative citing papers
OPSDL improves long-context LLM performance by having the model self-distill from its short-context capability using point-wise reverse KL divergence on generated tokens, outperforming SFT and DPO on benchmarks without harming short-context abilities.
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
Decomposing long-context reasoning into atomic skills, synthesizing targeted pseudo-datasets, and applying RL improves LLM performance on long-context benchmarks by an average of 7.7%.
Unifying LLM long-context optimizations into a four-step memory pipeline and offloading sparse/irregular stages to FPGA yields up to 2.2× speedup and 4.7× energy savings versus GPU-only.
citing papers explorer
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LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards
LongTraceRL trains LLMs on long-context reasoning by generating tiered-distractor data from search trajectories and using positive-only entity rubric rewards for process supervision.
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OPSDL: On-Policy Self-Distillation for Long-Context Language Models
OPSDL improves long-context LLM performance by having the model self-distill from its short-context capability using point-wise reverse KL divergence on generated tokens, outperforming SFT and DPO on benchmarks without harming short-context abilities.
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Towards Self-Improving Error Diagnosis in Multi-Agent Systems
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
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A Decomposition Perspective to Long-context Reasoning for LLMs
Decomposing long-context reasoning into atomic skills, synthesizing targeted pseudo-datasets, and applying RL improves LLM performance on long-context benchmarks by an average of 7.7%.
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Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
Unifying LLM long-context optimizations into a four-step memory pipeline and offloading sparse/irregular stages to FPGA yields up to 2.2× speedup and 4.7× energy savings versus GPU-only.