REVIEW 3 cited by
Efficient Joint Prediction of Multiple Future Tokens
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this short report, we introduce joint multi-token prediction (JTP), a lightweight modification of standard next-token prediction designed to enrich hidden state representations by jointly predicting multiple future tokens. Unlike previous multi-token prediction approaches, JTP strategically employs teacher forcing of future-tokens through a carefully designed representation bottleneck, allowing the model to encode rich predictive information with minimal computational overhead during training. We show that the JTP approach achieves a short-horizon belief state representation, while popular alternatives for multi-token prediction fail to do so. We demonstrate the effectiveness of our method on the synthetic star graph navigation task from from Bachmann and Nagarajan [2024], highlighting a significant performance improvement over existing methods. This manuscript presents promising preliminary results intended to stimulate further research.
Forward citations
Cited by 3 Pith papers
-
Hierarchical Latent Prediction for Language Models
HiLP adds a hierarchical latent prediction objective to LM pretraining, improving coding and multi-step reasoning benchmarks and speculative decoding acceptance, with zero inference-time overhead.
-
How Transformers Learn to Plan via Multi-Token Prediction
Multi-token prediction induces a two-stage reverse reasoning process in Transformers via gradient decoupling, improving planning on synthetic and realistic tasks.
-
Next-Latent Prediction Transformers Learn Compact World Models
NextLat augments next-token prediction with latent next-state prediction, theoretically converging latents to belief states and showing empirical gains in world modeling, reasoning, planning, and faster inference via ...
Discussion (0). Continue with ORCID to comment.