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Reinforcement learning (RL)-based post-training has become an effective approach for eliciting reasoning capabilities in large language models (LLMs). However, adapting post-RL models to new knowledge domains or behaviors through subsequent supervised fine-tuning (SFT) can severely overwrite these capabilities. Existin...
Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every layer needs the same rank budget. Further, some methods assume that rank budget needs to be split equally among the tasks too. We show this uniform-bud...
Guided Retrieval Training (GRT) is introduced, a novel method that improves the performance of a search agent by restricting the retrieval process during RL training using ground truth information, and enhances training efficiency by achieving better QA performance with fewer training steps.
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