AI agents are increasingly deployed as persistent operational systems, but do they remain reliable over time? Unfortunately no, our new work shows agents can quietly fail after deployment, despite passing day-1 evaluation. We call this "agent aging", akin to human aging.

Introducing MITS: Smarter, more efficient LLM reasoning with information theory This novel framework guides LLM tree search using Pointwise Mutual Information (PMI) to instantly evaluate reasoning steps. It achieves superior performance with high computational efficiency.

Model merging is a popular research topic with applications to LLM alignment and specialization. But, did you know this technique has been studied since the 90s? Here’s a brief timeline… (Stage 0) Original work on model merging dates back to the 90s [1], where authors showed that taking an average of neural network parameters yields a model that performs similarly to averaging the output of multiple neural networks (i.e., an ensemble). (Stage 1) Averaging along the training trajectory. Several works in the mid-to-late 2010s explore the idea of taking an average of model checkpoints throughout the training process. This can be done via an exponential moving average [2] or by just averaging specific checkpoints during training [3] and improves training stability / performance / generalization in certain cases. (Stage 2) Linear mode connectivity [4, 5] is a research topic–coming from research on neural network pruning / sparsity–that is highly related to model merging. Linear mode connectivity shows us that multiple neural networks (finetuned from the same base model) have a linear path of non-increasing loss between them in the parameter space. Put simply, interpolating between two finetuned models yields another model that also performs well. [6] studies this topic in the context of LLMs. (Stage 3) Weight averaging. Based on findings in linear mode connectivity research, we began to see several papers that directly average the parameters of several neural networks obtained in separate training runs (i.e., “model soups”) [7, 8, 9]. This strategy–although simple–was found to have several benefits in terms of model performance and generalization capability, somewhat similarly to creating an ensemble of models from several separate finetuning runs. (Stage 4) Better merging approaches. After initial explorations of model merging, we began to see several research papers that propose more specialized / effective merging techniques, such as: - Using task vectors to merge models [10]. - The TIES [11] or DARE [12] strategies for reducing interference during merging. These works show that model merging is highly effective for improving performance and combining the capabilities of multiple models. However, performance depends on the strategy we use for merging, which can be optimized to reduce conflicts / interference. (Stage 5) LLM-specific research. More recently, we have seen a widespread adoption of model merging within LLM research to combine LLM capabilities [13], improve the alignment process [14], or train better reward models [15]. One especially notable application of model merging in recent research is the use of WARP [14]–a state-of-the-art model merging technique for improving LLM alignment–for aligning Gemma-2 [16].

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