TL;DR
ContextLM introduces a novel multi-token prediction framework that enhances language modeling efficiency by predicting next contexts, leading to better performance with fewer parameters and improved downstream task generalization.
Contribution
It proposes a new context-level language modeling approach that improves efficiency and performance over standard models by learning predictive context embeddings.
Findings
Achieves baseline perplexity with 39% fewer parameters.
Outperforms standard models in downstream tasks.
Shifts the scaling law efficiency frontier.
Abstract
We propose ContextLM, a framework that implicitly learns multi-token prediction by augmenting standard pretraining with an intrinsic next-context prediction objective. ContextLM builds a language model on top of context embeddings that span multiple tokens, enabling better next-token prediction by predicting the next context. Our model is fully compatible with standard autoregressive, token-by-token evaluation paradigms (e.g., perplexity). Extensive experiments with GPT-2 and Pythia backbones (up to 1.5B parameters and 300B training tokens) reveal that ContextLM shifts the Pareto frontier of scaling laws, exhibiting superior efficiency in parameters, training tokens, and FLOPs. Our results show that ContextLM could already achieve the baseline perplexity using 39\% fewer parameters and demonstrates robust generalization improvements on extensive downstream tasks under equivalent…
Peer Reviews
Decision·ICLR 2026 Conference Desk Rejected Submission
The paper introduces a novel approach to augment existing language model training through chunk prediction. The results on the upstream perplexity is reasonably strong. The analysis are rather comprehensive with models of different families and sizes. The ablation study on the architectural components also support the design choices.
The paper is missing discussions on decoding in the main text: since the decoder takes the concatenation of hidden states and contexts, and the decoder uses causal attention, would that require recomputing the KV cache for the initial chunk representations when a new hidden state is prepended to it? That is, when adding h_T before c_init in the concatenated representation, the c_init would need to recalculate its KV after attending to this new hidden state h_T, which results in additional comput
1. The motivation for this work is very sound, with a simple framework to support finetuning on top of existing standard transformer architectures. 2. Both downstream and perplexity are calculated. 3. Attempted models up to 1.5B, which spans a lot of sizes for completeness under the small model regions. 4. Ablate on different aspects such as the choice of chunk size, length extension, and attention visualization.
1. The baseline should ideally be finetuned with the same data for fairness even though the data used for finetuning might be the same as those used for training Pythia and GPT2. 2. For different model sizes, the extra parameters introduced should be taken into account, especially for smaller size models, as people can expect more dramatic improvements for introducing two extra layers for 70M compared to 1.4B. This is also semi-indicated by the relative performance gain since bigger models migh
The idea of predicting context embeddings is novel and well-motivated. It effectively bridges the gap between token-level modeling and high-level semantic abstraction. The proposed method is a plug-and-play module that incurs relatively small computational overhead. Experimental results show consistent gains with minimal FLOP and memory cost, suggesting good potential for real-world adoption.
While the idea is intuitive, the paper lacks a formal analysis explaining why context embedding prediction enhances token-level modeling. Since the training objective remains NTP, the mechanism of improvement warrants deeper theoretical justification. All experiments are limited to models with up to 1.5B parameters. It remains unclear whether the observed gains generalize to larger-scale models. Comparisons with other relevant baseline methods are missing and should be included to strengthen the
1. The paper proposes integrating high-level context into the model's forward pass, akin to a global residual connection. 2. The results, especially those shown in Table 5, are compelling. ContextLM consistently outperforms GPT2 across different model scales and across a wide downstream NLP tasks. 3. ContextLM is fully compatible with the standard autoregressive architecture. It means the community can easily adopt this technique.
1. Where exactly is the Context Predictor integrated into the model? The paper doesn't specify if it's in the shallow or deep layers. It would be valuable to include an ablation study on how its placement (early vs. late layers) impacts overall performance. 2. The current comparison between ContextLM and the vanilla model is not completely fair, as ContextLM includes two extra Context Predictor layers (and thus more parameters/computation). The authors should compare ContextLM against a vanilla
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