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Confident Adaptive Language Modeling
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Confident Adaptive Language Modeling
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Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks. These gains come with a drastic increase in the models' size, potentially leading to slow and costly use at inference time. In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty. While certain predictions truly benefit from the models' full capacity, other continuations are more trivial and can be solved with reduced compute. In this work, we introduce Confident Adaptive Language Modeling (CALM), a framework for dynamically allocating different amounts of compute per input and generation timestep. Early exit decoding involves several challenges that we address here, such as: (1) what confidence measure to use; (2) connecting sequence-level constraints to local per-token exit decisions; and (3) attending back to missing hidden representations due to early exits in previous tokens. Through theoretical analysis and empirical experiments on three diverse text generation tasks, we demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $\times 3$ -- while provably maintaining high performance.
Forward citations
Cited by 10 Pith papers
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Tapered Language Models
Tapered Language Models monotonically decrease MLP width across depth with a cosine schedule, yielding better perplexity and downstream performance than uniform-width baselines across multiple architectures and scales...
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Training per-layer affine probes on frozen transformers yields more reliable latent predictions than the logit lens and enables detection of malicious inputs from prediction trajectories.
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Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers
In a depth-recurrent transformer, each token converges to a fixed point at its own rate; a parameter-free early-exit rule reads this and matches depth-8 quality at 4.94 average loops, beating a learned router.
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Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readouts
In looped transformers, halting-gate failures come mainly from how gate training reshapes the trajectory; fixed-prior depth supervision plus simple confidence readouts yields better accuracy per unit of compute.
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RKSC: Reasoning-Aware KV Cache Sharing and Confident Early Exit for Multi-Step LLM Inference
RKSC delivers 3.008x mean speedup over baseline and 1.66x over vLLM prefix caching for multi-branch LLM reasoning via similarity-based KV sharing and confidence-gated early exit, with 0.37% error rate.
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Sparse Layers are Critical to Scaling Looped Language Models
Looped MoE models scale better than standard transformers because different experts activate on each loop pass, recovering expressivity without extra parameters, and support superior early exits.
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Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
An early-exit rule with a zero-shot fallback, calibrated by Learn-then-Test risk control, keeps the average loss from corrupted in-context demonstrations under a preset bound.
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Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Calibration from Ranking
SBBT separates Brier-score calibration gains from AUROC ranking gains in prefix-conditioned success estimation for LLM math reasoning, with structure-aware signals yielding up to +0.110 AUROC over baselines.
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Sparse Layers are Critical to Scaling Looped Language Models
Looped-MoE models scale better than dense looped or standard transformers because routing changes across loops, and they enable stronger compute-quality trade-offs via early exits at loop boundaries.
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CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models
Presents CosmicFish-HRM, a compact LM using hierarchical recurrent reasoning to adapt computation depth per input.
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