Counteractive RL vs. SEED RL and TIS: How Algorithmic Efficiency Impacts Hardware in Scalable Deep RL

 

Here’s an explanation of what the NeurIPS 2025 paper “Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning” means — especially for hardware — based on the official abstract and paper content: OpenReview+1

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🔍 What the Paper Is About

The paper introduces Counteractive Reinforcement Learning, a new paradigm for improving efficiency and scalability in deep reinforcement learning (RL). It focuses on how agents interact with environments in complex, high‑dimensional settings (like advanced games or simulators) and proposes a method that:

• Is theoretically founded and justified. OpenReview

• Enhances training acceleration and sample efficiency without adding computational complexity. OpenReview

• Shows empirical gains on typical RL benchmarks (e.g., Atari‑like environments) in high‑dimensional spaces. OpenReview

The core idea is that instead of relying purely on standard explorative actions or simple heuristics, Counteractive RL uses counteractive actions derived from the agent’s experiences to guide learning more efficiently. OpenReview

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⚙️ What This Means for Hardware

Here’s how it translates into hardware‑level implications:

🧠 1. Better Sample Efficiency → Less Compute Needed

One of the biggest costs in RL (especially deep RL) is environment interaction — generating trajectories (rollouts) and then training from them.

• If the algorithm uses fewer samples to learn the same amount, then it reduces demands on the sampler and learner hardware.

• That means fewer GPU/TPU hours, less memory bandwidth used for rollouts, and reduced overall cost for RL training jobs.

Implication: More efficient algorithms like Counteractive RL can lower compute resource requirements even if the underlying hardware stays the same.

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🚀 2. Zero Additional Computational Complexity

The paper claims its method improves training speed without adding extra computational cost beyond standard RL training. OpenReview

Hardware meaning:

• You don’t need new accelerators or special chips.

• Existing GPUs/TPUs designed for matrix operations and neural network backpropagation are still sufficient.

• This is attractive for data centers and cloud training infrastructure where every extra FLOP counts.

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📈 3. Scalability Without Specialized HW

Many RL scaling efforts (e.g., SEED RL) benefit from hardware architectures optimized for throughput and distributed inference. arXiv Counteractive RL, by focusing on algorithmic improvements at the level of efficient experience use, means that:

• Efficient use of parallel simulation environments matters less if each experience is more informative.

• Scale‑out hardware costs (like more GPUs or nodes) grow more slowly as you increase environment complexity.

Implication: Characteristic RL workloads (environment simulators + neural nets) can run more efficiently on the same hardware stack.

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🧩 4. Indirect Hardware Impact: Less Pressure on Sampling Systems

Efficient RL methods help reduce the stress on the sampler engine (fast inference component). In previous work (like SEED RL), making inference (sampling) fast and scalable was hardware‑intensive because the sampler had to keep up with the learner. arXiv

Counteractive RL’s focus on better strategic data use can mean:

• Fewer simulated steps per useful update

• Less need for excessively large simulation farms or massive CPU/GPU clusters just to generate experience

Real implication: Cost savings not just in GPUs/TPUs for learning, but also in CPUs/GPUs used for environment simulation.

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🧠 Summary for Hardware Folks

RL Challenge	Counteractive RL Impact	Hardware Implication
High sample complexity	Reduced samples needed	Lower compute load on sampler & learner
Large GPU/TPU use	Same complexity but more efficiency	Better ROI per GPU hour
Simulation bottlenecks	Better use of each rollout	Less need for large simulator clusters
Scalability concerns	Maintains performance as tasks scale	More efficient use of existing hardware

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🧠 Why This Matters

Even if the method doesn’t require new hardware, it makes existing hardware more effective for RL — a huge benefit because RL workloads are some of the most compute‑heavy and expensive in modern AI research. This kind of improvement lets research teams:

• Train stronger agents with fewer resources

• Scale to harder tasks without needing more GPU/TPU clusters

• Save money and energy in large data centers

Let’s compare Counteractive RL with other scalable RL approaches like SEED RL and Truncated Importance Sampling (TIS), focusing specifically on hardware implications:

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1️⃣ SEED RL – Hardware-Centric Scalability

What it does:

• SEED RL focuses on decoupling inference (sampler) from training (learner).

• Uses fast inference engines to generate huge numbers of rollouts in parallel, while learners update policies on distributed GPUs/TPUs.

• Heavy emphasis on hardware throughput and parallelization.

Hardware Implications:

• Requires lots of CPUs/GPUs just to maintain throughput.

• Optimized for tensor cores, high-speed interconnects, distributed training.

• Scaling SEED RL often means buying more hardware, not necessarily improving algorithmic efficiency.

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2️⃣ Truncated Importance Sampling (TIS)

What it does:

• Fixes distribution mismatch between sampler and learner by weighting gradients with a capped importance ratio.

• Improves policy updates without changing rollout generation speed.

Hardware Implications:

• Minor additional computation (gradient scaling), but negligible compared to backprop.

• Works well with heterogeneous hardware, since it corrects for differences between fast sampler engines and slower learners.

• Main gain is software correctness and stability, not raw speed or reduced compute.

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3️⃣ Counteractive RL – Algorithmic Efficiency First

What it does:

• Focuses on using experiences more strategically, reducing the number of samples needed to achieve strong learning.

• Emphasizes sample efficiency and scalability over hardware parallelism.

• Works with existing RL pipelines; does not require specialized sampler/learner separation.

Hardware Implications:

• Reduces total compute cost: fewer rollouts, fewer gradient updates for same performance.

• Less pressure on simulation farms and GPU clusters.

• Makes RL training cheaper and more energy-efficient, even without buying new hardware.

• Can still scale to harder tasks without additional infrastructure.

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✅ Quick Comparison Table

Method	Hardware Focus	Sample Efficiency	Key Hardware Benefit
SEED RL	Max throughput, distributed GPUs/TPUs	Medium	Requires lots of hardware for scaling
TIS	Stability across engines	Medium	Works with heterogeneous hardware, small extra compute
Counteractive RL	Algorithmic efficiency	High	Reduces total compute, fewer GPUs/TPUs needed, cheaper simulations

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⚡ Key Takeaways for Hardware

1. SEED RL: Scale via hardware → more GPUs/TPUs, high-speed interconnects.

2. TIS: Minor computation overhead → ensures correctness, works on current hardware.

3. Counteractive RL: Scale via smarter algorithms → fewer samples, less compute, same or better final performance.

Bottom line: Counteractive RL is hardware-friendly because it optimizes how you use existing hardware, rather than demanding more of it.

Why TIS Matters in RL: Boosting Performance Despite Lower Logged Rewards

 learn & explore math with python

https://cme295.stanford.edu/ - Transformers & Language Models

Truncated importance sampling

1. Two engines in RL training

The text describes two separate engines:

1. Sampler engine – generates rollouts (sequences of actions/states) using the current policy.

   • Runs fast because inference is cheaper than training.

   • Uses frameworks like vLLM / SGLang.

   • Optimized for speed, not perfect numerical fidelity.

2. Learner engine – computes policy updates (backpropagation and gradient updates).

   • Uses frameworks like FSDP / DeepSpeed for distributed training.

   • Runs slower because it does heavy computation (matrix multiplications, gradients).

Hardware angle:

• Sampler engine is CPU/GPU-light, optimized for high throughput of inference.

• Learner engine is GPU-heavy, uses distributed GPUs with high memory bandwidth and compute.

• Most RL training cost is in inference (sampler), so making it fast saves a lot of GPU hours.

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2. The mismatch problem

• The sampler and learner engines aren’t identical.

• They might produce slightly different token probabilities due to optimizations, quantization, or implementation differences.

• This means the data used to train (from sampler) is slightly off-policy for the learner.

Hardware angle:

• Differences could come from precision optimizations (e.g., FP16, BF16, INT8) in sampler vs full-precision gradients in learner.

• Different hardware (TPU vs GPU, or GPU with tensor cores vs CPU) can amplify these mismatches.

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3. Truncated Importance Sampling (TIS)

• TIS corrects the mismatch by weighting gradients according to the ratio of target (learner) probabilities to sampler probabilities.

• The “truncated” part caps the ratio to avoid extreme values destabilizing training.

Hardware angle:

• TIS introduces some extra computation, but mostly involves scaling gradients—not a huge overhead on modern GPUs.

• The main benefit is stability and correctness, allowing distributed and mixed-precision hardware setups to work reliably.

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4. Why logged rewards can appear lower

• Training reward is logged from the sampler’s rollouts, not the learner.

• TIS corrects for learner expectation, so the updates are better for final performance, even if the sampler’s logged reward temporarily looks lower.

Hardware angle:

• This is purely software/statistical, no direct impact on hardware.

• But it highlights why fast samplers are critical: even if they are slightly inaccurate, TIS ensures the GPU-heavy learner still gets correct gradient updates.

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✅ Key hardware takeaways

1. Sampler needs to be extremely fast – optimized inference frameworks and hardware acceleration (e.g., tensor cores, GPU/CPU vectorization).

2. Learner is heavy – uses GPUs/TPUs for backpropagation; distributed training frameworks are critical.

3. Mismatch is inevitable on heterogeneous hardware – precision differences, library differences, or hardware-specific optimizations can cause small distribution shifts.

4. TIS is a software solution to hardware heterogeneity – it compensates for differences without needing extra high-end hardware.

5. Logging artifacts – lower training reward is not a hardware problem, just a reflection of the mismatch between fast sampler and slower learner.

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💡 TL;DR for hardware:
TIS doesn’t require new hardware—it’s a smart software trick to handle hardware-induced mismatches between a fast inference engine (sampler) and a heavy GPU-based learner. It allows you to use very fast, optimized inference hardware without hurting final RL performance, even if the training logs look worse temporarily.

Disclaimer:
For educational purposes only— chatgpt explaining RL concepts and hardware trade-offs, not professional advice.

TTT - Test time training

 End-to-End Test-Time Training for Long Context - Continual learning in language models.

I’ll translate this strictly into hardware terms: what changes on the chip, in memory, and in execution when you allow test-time training (TTT).

No hype, just silicon reality.

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1. Traditional hardware assumption (what chips are built for)

Clear phase separation

Training

• Forward pass

• Backward pass (gradients)

• Weight updates

• Huge memory + compute

Inference

• Forward pass only

• Read-only weights

• Deterministic latency

• Optimized for throughput

➡️ Every accelerator today assumes this separation.

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2. What this paper breaks

“At test-time, our model continues learning from context.”

Hardware translation:

• Inference now includes gradient computation

• Weights are no longer read-only

• Backward pass is happening during inference

• Optimizer state exists at inference time

This is a fundamental architectural shift, not a software tweak.

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3. What “compressing context into weights” means physically

Normally

Long context ⇒

• Store tokens in KV cache

• Memory grows linearly with context length

• Attention becomes bandwidth-bound

With end-to-end TTT

Context ⇒

• Forward pass

• Backward pass

• Small number of weight updates

Then:

• Context information lives in weights

• KV cache can be truncated or discarded

• Model carries information implicitly

📌 Hardware interpretation:

You trade memory bandwidth for compute.

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4. Execution timeline on hardware

Standard inference

load weights → forward → output

End-to-end test-time training

load weights
for chunk in context:
    forward
    backward
    weight update
forward on query

This looks like micro-training loops inside inference.

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5. What new hardware requirements emerge

1. Writable weight memory

• SRAM / HBM must support:

  • frequent writes

  • fine-grained updates

• Weight immutability assumption is broken

2. Gradient storage

You now need:

• Activation buffers

• Gradient buffers

• Possibly optimizer state (e.g. Adam moments)

Even if small, this was never provisioned for inference.

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6. Compute pattern shift

Inference chips are optimized for:

• GEMMs

• No data dependencies

• No backward pass

TTT requires:

• Reverse-mode autodiff

• Transpose GEMMs

• Reduction ops

• Update kernels

Which means:

• Training hardware features leak into inference

• Inference-only accelerators become obsolete

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7. Why this helps long context (hardware explanation)

Long context usually fails because:

• KV cache explodes

• Memory bandwidth saturates

• Attention cost grows

TTT replaces:

Store all tokens forever

with:

Summarize context into weights

So:

• Memory footprint shrinks

• Compute grows but stays local

• No quadratic attention explosion

This is a compute-for-memory exchange, which is favorable on modern silicon.

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8. Why “end-to-end objective” matters for hardware

Because:

• Same loss

• Same kernels

• Same dataflow

This allows:

• Unified graph

• No mode switching

• Fewer special cases

➡️ Easier to compile
➡️ Easier to pipeline
➡️ Easier to schedule

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9. Implications for future accelerators

This paper quietly implies the need for:

🔹 “Always-train” accelerators

• Training + inference merged

• Fine-grained weight updates

• Persistent state across requests

🔹 On-device continual learning

• Especially for:

  • robotics

  • agents

  • streaming inputs

  • embodied systems

🔹 New memory hierarchy

• Fast local weight buffers

• Checkpoint / rollback support

• Weight versioning

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10. Big picture (hardware takeaway)

Language models stop being static programs and become adaptive systems.

Hardware must evolve from:

• “Load weights, run, discard”
  to:

• “Run, adapt, remember, continue”

This is as big a shift as:

• CNNs → Transformers

• Static graphs → dynamic graphs

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One-sentence summary

End-to-end test-time training turns inference hardware into mini training systems, trading memory bandwidth for compute and forcing accelerators to support writable weights, gradients, and continual adaptation.

• Compare this to KV-cache hardware costs

• Explain how this maps to TPUs vs GPUs vs NPUs

• Speculate what an ideal chip for TTT would look like

• Connect this to neuromorphic or in-memory compute

System Verilog as concurrent reactive system

 SystemVerilog absolutely supports recognizable design patterns and parallel programming patterns, but they look different from software patterns because they’re constrained by time, concurrency, and hardware semantics. If you approach SV expecting classic OO or threading models, it feels odd; if you approach it as a concurrent reactive system, the patterns are very clear.

Below is a structured way to think about both.

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1. Design patterns in SystemVerilog (conceptual mapping)

SystemVerilog uses behavioral + structural + temporal patterns rather than purely OO ones.

1.1 Structural / OO-inspired patterns (mostly in testbenches)

These are common in UVM and non-UVM benches.

Factory

• UVM factory, or manual new() indirection

• Purpose: late binding of components

Strategy

• Virtual methods or interfaces

• Example: interchangeable drivers or scoreboards

Observer

• Analysis ports / callbacks

• Event-based monitors

Adapter

• Wrapping a DUT interface with a class-based API

Template Method

• Base class defines flow

• Derived classes override hooks

These work well because classes are for control, not data paths.

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2. Hardware-native “design patterns” (more important than OO ones)

These are patterns you see repeatedly in RTL and cycle-based logic.

2.1 Pipeline pattern

• Staged registers

• Valid/ready propagation

• Latency-aware transformations

Key idea: spatial + temporal decomposition

2.2 FSM (State Machine)

• Control-dominated behavior

• Often paired with datapaths

Variants:

• One-hot FSM

• Encoded FSM

• Hierarchical FSM

2.3 Producer–Consumer

• FIFOs

• Mailboxes (TB)

• Valid/ready interfaces

2.4 Arbiter / Scheduler

• Round-robin

• Priority-based

• Time-sliced access

2.5 Decoupling via interfaces

• interface + modports

• Separates timing, direction, and ownership

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3. Parallel programming patterns in SystemVerilog

This is where SV is very strong.

3.1 Static parallelism (hardware concurrency)

This is “always-on” concurrency.

Examples:

• Multiple always_ff blocks

• Parallel datapaths

• Independent clock domains

Pattern:

Everything runs concurrently; sequencing is explicit.

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3.2 Dynamic parallelism (simulation-time)

Used heavily in testbenches.

fork–join patterns

fork
  task_a();
  task_b();
  task_c();
join

Variants:

• join – wait for all

• join_any – wait for one

• join_none – fire and forget

Pattern analogs:

• Fork–join (classic parallel programming)

• Futures/promises (via events/mailboxes)

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3.3 Pipeline parallelism (temporal overlap)

Multiple operations in flight, separated by cycles.

Pattern:

• Each stage works independently

• Backpressure via ready/valid

This mirrors software pipeline parallelism but is cycle-accurate.

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3.4 Task-level parallelism (TB side)

Common TB pattern:

• Driver

• Monitor

• Scoreboard

• Coverage

Each runs independently and communicates via:

• Mailboxes

• Queues

• Events

• Analysis ports

This is essentially an actor model.

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4. Synchronization primitives = parallel patterns

SystemVerilog provides native constructs that directly encode parallel patterns:

Construct	Pattern
event	Signal / condition variable
mailbox	Producer–consumer
semaphore	Mutual exclusion / resource pool
process	Task handle / thread control
wait fork	Barrier

These map cleanly to classical parallel programming concepts.

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5. What doesn’t map well from software patterns

Important to know the limits:

• Shared-memory locking is rare in RTL

• No preemption (time advances only at events)

• No true race-free shared variables without clocking

• No implicit sequential execution order

So patterns like:

• Reader–writer locks

• Lazy initialization

• Thread pools

…are either awkward or meaningless in RTL, but sometimes useful in testbenches.

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6. Mental model shift (key insight)

SystemVerilog patterns are:

• Time-aware

• Explicitly concurrent

• Dataflow-driven

Instead of asking:

“What design pattern is this?”

Ask:

“What runs in parallel, what synchronizes them, and what advances time?”

That framing makes SV patterns obvious and intentional.

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7. If you’re using LLMs with SystemVerilog (important tie-in)

To get good results, tell the LLM:

• Whether you’re writing RTL or TB

• Whether parallelism is cycle-based or simulation-based

• What synchronization primitives are allowed

Example meta-prompt:

Prefer established SystemVerilog concurrency patterns
(fork–join, producer–consumer, pipeline, actor-style TB components).
Avoid serializing inherently parallel behavior.

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• Map classic Go / C++ / OpenMP parallel patterns to SystemVerilog

• Show anti-patterns that cause line-count blowups

• Help define a canonical pattern catalog for your codebase so the LLM stays consistent

What is projection?

 because “projection” is one of those words that sounds abstract but is actually very concrete in math and hardware.

I’ll explain it in layers and keep it grounded.

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1. Projection in plain language

A projection means:

Taking a high-dimensional thing and expressing it using fewer, chosen directions.

Think:

• You have a 3D object

• You shine a light

• The shadow on the wall is a projection

You didn’t keep all the information — only what lies along certain directions.

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2. Mathematical definition (no ML yet)

Given:

• A vector $x \in \mathbb{R}^d$

• A direction vector $w \in \mathbb{R}^d$

The projection of x onto w is:

$$\text{proj}_w(x) = (x \cdot w)$$

That single number tells you:

“How much of x points in the direction w?”

If you use multiple directions $w_1, w_2, \dots, w_s$:

$$\text{Projection}(x) = \begin{bmatrix} x \cdot w_1 \\ x \cdot w_2 \\ \vdots \\ x \cdot w_s \end{bmatrix}$$

This is exactly a matrix multiply:

$$y = x W \quad \text{where } W \in \mathbb{R}^{d \times s}$$

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3. Projection in neural networks

In Transformers:

• Input vector: token embedding $x \in \mathbb{R}^d$

• Projection matrix: $W \in \mathbb{R}^{d \times s}$

Compute:

$$y = xW$$

This is called:

• Linear projection

• Feature projection

• Embedding projection

Nothing mystical — just dot products.

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4. Projection in self-attention

Queries, Keys, Values are projections

Given token embedding $x$:

$$Q = x W_Q$$ $$K = x W_K$$ $$V = x W_V$$

Each $W$:

• Selects different directions

• Emphasizes different aspects of the same token

So:

• Q = “What am I looking for?”

• K = “What do I contain?”

• V = “What information do I pass along?”

All three are just projections of x.

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5. Projection in Primal-Attention (your context)

Instead of building a huge matrix $QK^T$:

• Choose a small set of directions (rank $s$)

• Project Q and K into that space:

$$Q' = Q W_q$$ $$K' = K W_k$$

Now interactions happen in a low-dimensional space.

This is why:

• Computation drops from O(N²) → O(N·s)

• Memory drops from quadratic → linear

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6. Hardware interpretation (important)

On hardware, projection = GEMM:

Input vectors  ──►  Matrix multiply  ──►  Smaller vectors

Key properties:

• ✔ Fully parallel

• ✔ Cache-friendly

• ✔ No control flow

• ✔ Streamable

This is why projections are hardware gold.

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7. Intuition with a real-world analogy

Imagine:

• Each token is a long list of properties (d = 1024)

• You only care about:

  • sentiment

  • tense

  • entity type

  • position

  • topic

Each of those is a direction.

Projection asks:

“How much does this token express each of these?”

That’s it.

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8. One-sentence takeaway

A projection is a learned change of coordinates that keeps only the directions you care about.

In math: dot products
In ML: linear layers
In hardware: matrix multiplies

• Projection vs embedding

• Projection vs PCA

• Why projections are learned instead of fixed

• How projection ≈ basis change