DeepSeek AI Researchers Present Engram: A Conditional Memory Axis for Sparse LLMs

Transformers use attention and Mix-Experts to measure computation, but they still lack a native way to perform information lookups. They recalculate the same local patterns over and over again, wasting depth and FLOPs. DeepSeek’s new Engram module addresses this gap by adding a conditional memory axis that works alongside the MoE rather than replacing it.

At a high level, an Engram improves the embedding of a classical N-gram and turns it into a lookup memory, which is O(1) directly connected to the Transformer core. The result is a parametric memory that stores static patterns like regular sentences and associations, while the spinal cord focuses on complex thinking and long-range interactions.

How Engram Fits DeepSeek Transformer

The proposed method uses the DeepSeek V3 token with a 128k vocabulary and a front train of 262B tokens. The core is a 30 block Transformer with a hidden size of 2560. Each block uses Multi head Latent Attention with 32 heads and connects to provide advanced networks by using Manifold Constrained Hyper Connections with expansion level 4. Optimization uses Muon optimizer.

The Engram attaches to this core as a small embedding module. It is built from accelerated N gram tables, with multiple hashing of main size buckets, a small variable depth than N gram core and a core aware scale in the range 0 to 1 that controls how much returned embedding is injected into each branch.

In the larger scale models, the Engram-27B and Engram-40B share the same Transformer core as the MoE-27B. MoE-27B replaces dense feeds with DeepSeekMoE, using 72 experts and 2 joint experts. Engram-27B reduces the number of sent specialists from 72 to 55 and also splits those parameters into a 5.7B Engram memory while keeping the total parameters at 26.7B. The Engram module uses N equal to {2,3}, 8 Engram heads, size 1280 and is included in layer 2 and 15. Engram 40B increases Engram memory to 18.5B parameters while keeping open parameters the same.

Sparity Allocation, Second Scaling Knob beside MoE

The main design question is how to divide the budget of the different parameters between the experts moved to the route and the conditional memory. The research team formalizes this as a Sparsity Allocation problem, the allocation ratio ρ defined as a fraction of the redundant parameters given by the MoE experts. A pure MoE model has ρ equal to 1. Decreasing ρ rescales the parameters from experts to Engram spaces.

For the average 5.7B and 9.9B models, the sweep ρ gives a clear U-shaped curve of the validation loss versus the distribution ratio. Engram models are similar to the pure MoE base even when ρ drops to about 0.25, which corresponds to about half of the most expert referrals. The best is seen when about 20 to 25 percent of the minimum budget is given to Engram. This maximum is stable for both computing systems, suggesting a strong separation between conditional computation and conditional memory under a fixed minimum.

The research team also studied a non-volatile memory system on a fixed core 3B MoE trained on 100B tokens. They measure the Engram table from about 2.58e5 to 1e7 slots. Validation loss follows an almost absolute power law in the input space, meaning that more conditional memory keeps paying off without additional computation. Engram also outperforms OverEncoding, another N gram encoding method with average word embedding, under the same memory budget.

Major Effects of Pre-Training

The main comparison involves four models trained on the same 262B token curriculum, with 3.8B parameters tuned in all cases. These are Dense 4B with 4.1B total parameters, MoE 27B and Engram 27B for 26.7B total parameters, and Engram 40B for 39.5B total parameters.

In The Pile test set, the language modeling loss is 2.091 for MoE 27B, 1.960 for Engram 27B, 1.950 for Engram 27B variant and 1.942 for Engram 40B. Loss of Dense 4B Pile is not reported. The validation loss for the hold within the set decreased from 1.768 for MoE 27B to 1.634 for Engram 27B and to 1.622 and 1.610 for Engram variants.

In all cognitive and reasoning benchmarks, the Engram-27B consistently outperforms the MoE-27B. MMLU increases from 57.4 to 60.4, CMMLU from 57.9 to 61.9 and C-Eval from 58.0 to 62.7. ARC Challenge increases from 70.1 to 73.8, BBH from 50.9 to 55.9 and DROP F1 from 55.7 to 59.0. Code and math functions also improve, for example HumanEval from 37.8 to 40.8 and GSM8K from 58.4 to 60.6.

Engram 40B generally pushes these numbers further although the authors note that it is likely under-trained for 262B tokens because its training loss continues to diverge from baseline near the end of pre-training.

Long-Term Content Behavior and Mechanical Effects

After pre-training, the research team expands the context window using YaRN to 32768 tokens in 5000 steps, using 30B long high-quality tokens. They compared MoE-27B and Engram-27B at test points corresponding to pre-41k, 46k and 50k training steps.

For LongPPL and RULER in the context of 32k, Engram-27B matches or surpasses MoE-27B under three conditions. With about 82 percent of the pre-training FLOPs, Engram-27B in 41k steps matches LongPPL while improving the accuracy of RULER, for example Multi Query NIAH 99.6 against 73.0 and QA 44.0 against 34.5. Under iso loss at 46k and iso FLOPs at 50k, Engram 27B improves both confusion and all RULER categories including VT and QA.

Mechanistic analysis using LogitLens and Average Kernel Alignment. The Engram variant shows a low-layer intelligent KL separation between the intermediate records and the final prediction, especially in the early blocks, which means that the representations become a ready prediction quickly. Similar CKA maps show that the shallower Engram layers correspond better to the deeper MoE layers. For example, layer 5 in Engram-27B corresponds to layer 12 in the base MoE. Taken together, this supports the idea that Engram effectively increases model depth by extracting static reconstructions from memory.

Ablation studies on a 12 model 3B MoE with 0.56B open frames add 1.6B Engram memory as a reference configuration, using N equal to {2,3} and including Engrams in layers 2 and 6. A sweep of the Engram one layer deep indicates that the early insertion into layer 2 is correct. The breakdown highlights three key pieces, multi-branch integration, context-aware gating and token compression.

The sensitivity analysis shows that the knowledge of the truth is very dependent on the Engram, and TriviaQA drops to about 29 percent of its original score when the output of the Engram is suppressed where it is assumed, while reading comprehension tasks retain about 81 to 93 percent of the performance, for example C3 at 93 percent.

Key Takeaways

1. Engram adds a conditional memory axis to split LLMs so that common N gram patterns and associations are retrieved in O(1) fast lookups, while Transformer core and MoE experts focus on dynamic reasoning and long-range dependencies.
2. Under the fixed parameter and FLOPs budget, reallocating about 20 to 25 percent of the minimum capacity from the MoE specialists to the Engram memory reduces the validation loss, indicating that conditional memory and conditional computation are complementary rather than competing.
3. To a large extent the pre-training of 262B tokens, Engram-27B and Engram-40B with the same effective parameters of 3.8B surpass the base MoE-27B in representing language, knowledge, thinking, coding and mathematical measurements, while keeping the structure of the Transformer core unchanged.
4. The extension of the long context on 32768 tokens using YaRN shows that Engram-27B matches or improves LongPPL and clearly improves RULER scores, especially Multi-Query-Needle in Haystack and variable tracking, even when trained with a lower or equal compute compared to MoE-27B.

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