Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX

## Abstract

Abstract Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable.
While modern video games may offer rich dynamics, they are computationally expensive and poorly suited for large-scale experimentation due to their CPU-bound execution.
We introduce Octax , a high-performance suite of classic arcade game environments implemented in JAX, based on CHIP-8 emulation, a predecessor to Atari, which is widely adopted as a benchmark in RL research. Octax provides the JAX community with a long-awaited end-to-end GPU alternative to the Atari benchmark, offering image-based environments, spanning puzzle, action, and strategy genres, all executable at
massive scale on modern GPUs.
Our JAX-based implementation achieves orders-of-magnitude speedups over traditional CPU emulators while maintaining perfect fidelity to the original game mechanics.
We demonstrate Octax ’s capabilities by
training RL agents across multiple games, showing significant improvements in
training speed and scalability compared to existing solutions. The environment’s modular design enables researchers to easily extend the suite with new games or generate novel environments using large language models, making it an ideal platform for large-scale RL experimentation.

## 1 Introduction

Modern reinforcement learning (RL) research (Sutton & Barto, 2018) demands extensive experimentation to achieve statistical validity, yet computational constraints severely limit experimental scale. RL papers routinely report results with fewer than five random seeds due to prohibitive training costs (Henderson et al., 2018; Colas et al., 2018; Agarwal et al., 2021; Mathieu et al., 2023; Gardner et al., 2025). While understandable from a practical standpoint, this undersampling undermines statistical reliability and impedes algorithmic progress. Environment execution creates this bottleneck: while deep learning has embraced end-to-end GPU acceleration, RL environments remain predominantly CPU-bound.
Originally designed under severe hardware constraints, classic arcade games represent a
solution for scalable RL experimentation.
The Atari Learning Environment (ALE) (Bellemare et al., 2013) has established itself as a standard RL benchmark, although existing implementations remain fundamentally CPU-bound.
As noted by Obando-Ceron & Castro (2020), the Rainbow paper (Hessel et al., 2018) required
34,200 GPU hours (equivalent to 1,425 days) of experiments, a computational cost that is prohibitively high for small research laboratories. In this paper, we propose an alternative approach for training RL agents in environments with mechanisms similar to ALE, with significantly reduced computational cost.

##### Contributions.

We introduce Octax (^1^11The repository containing all source code, experiments, and data is available at: [https://github.com/riiswa/octax](https://github.com/riiswa/octax)), a suite of arcade game environments implemented in JAX (Bradbury et al., 2018a) through CHIP-8 emulation. CHIP-8, a 1970s virtual machine specification contemporary with early Atari systems, became the foundation for numerous classic games spanning puzzle, action, and strategy genres.
CHIP-8’s constraint-driven design creates games with similar cognitive demands to Atari while enabling efficient vectorized emulation that scales to thousandsof parallel instances.
The JAX ecosystem has rapidly emerged as a solution for scalability in RLresearch but lacks native environments, particularly image-based ones.
Our framework addresses this gap by transforming classic games into fully vectorized, GPU-accelerated simulations.
Thesesimulations run thousands of game instances in parallel while maintaining perfect fidelity to the
original mechanics. This approach
dramatically reduces experiment times. Experiments that previously required days or weeks can now be completed in hours. This efficiency makes comprehensive hyperparameter sweeps and ablation studies computationally feasible.
The modular design facilitates extension with new games or automated generation using large language models that can directly output CHIP-8 assembly code. Figure [1](https://arxiv.org/html/2510.01764v2#S1.F1) provides an overview of the integrated CHIP-8 games.

##### Outline.

First, we present the our end-to-end JAX implementation of classic arcade environments through CHIP-8 emulation (Section [3](https://arxiv.org/html/2510.01764v2#S3)). Second, we demonstrate diverse learning dynamics through PPO evaluation across 16 games (Section [4.1](https://arxiv.org/html/2510.01764v2#S4.SS1)). Third, we achieve 350,000 environment steps per second (1.4 million frames per second) on consumer-grade hardware, substantially outperforming CPU-based solutions (Section [4.2](https://arxiv.org/html/2510.01764v2#S4.SS2)). Fourth, we establish an LLM-assisted pipeline for automated environment generation that creates meaningful difficulty gradients (Section [4.3](https://arxiv.org/html/2510.01764v2#S4.SS3)).

Figure: Figure 1: Overview of CHIP-8 game environments implemented in Octax.
Refer to caption: figures/mosaic.png

## 2 Related Work

Game environments have proven essential for RL research because they provide engaging, human-relevant challenges with clear success metrics. The Arcade Learning Environment (ALE) Bellemare et al. (2013) demonstrated this principle by establishing Atari 2600 games as the standard RL benchmark, enabling breakthrough algorithms like DQN (Mnih et al., 2015) and Rainbow (Hessel et al., 2018). The success of these classic arcade games stems from their constraint-driven design: simple rules that yield complex behaviors, deterministic dynamics that enable reproducible experiments, and visual complexity that tests spatial reasoning without overwhelming computational resources.

While algorithmic advances demand increasingly large-scale experiments with thousands of parallel environments and extensive hyperparameter sweeps, traditional game environments remain CPU-bound and poorly suited for parallel execution. This mismatch has driven a progression of solutions, each addressing different aspects of the scalability problem.

Game-based RL environment platforms.
Increasingly sophisticated gaming platforms have been developed to test different dimensions of learning performance. NetHack Learning Environment (Küttler et al., 2020) provides procedurally generated roguelike challenges that test long-term planning, while Crafter (Hafner, 2021) offers simplified Minecraft-like environments focused on resource management. These environments expand cognitive challenges beyond arcade games, but their CPU-based implementations compound the scalability problem.

CPU high-performance solutions.
Several projects have focused on optimizing CPU-based environment execution. EnvPool (Weng et al., 2022) achieves substantial speed improvements through highly optimized C++ implementation, demonstrating up to 1 million Atari frames per second on high-end hardware. PufferLib (Suarez, 2025) provides environments written entirely in C, achieving millions of steps per second through over 20,000 lines of optimized code. While these approaches improve CPU throughput, they retain fundamental limitations: costly CPU-GPU data transfers during training and require C implementation in a Python-dominated field.

GPU-accelerated RL environments.
GPU-accelerated solutions target the constraint more directly by moving environment execution to accelerators. CUDA Learning Environment (CuLE) (Dalton et al., 2020) provides a pioneering CUDA port of ALE, achieving 40-190 million frames per hour on single GPUs. Isaac Gym (Makoviychuk et al., 2021) demonstrates similar principles for robotics tasks, achieving 2-3 orders of magnitude speedups over CPU approaches by running thousands of environments simultaneously. These GPU approaches solve computational bottlenecks but introduce NVIDIA hardware dependence and substantial per-environment engineering costs.

JAX-based environments.
The adoption of JAX (Bradbury et al., 2018b) has enabled natively accelerated environments that combine portability across hardware with end-to-end GPU acceleration. Brax (Freeman et al., 2021) established viability through MuJoCo-like physics simulation, while Gymnax (Lange, 2022) provides JAX implementations of classic control tasks and simplified environments from BSuite (Osband et al., 2019) and MinAtar (Young & Tian, 2019). Specialized environments target specific research needs: XLand-MiniGrid (Nikulin et al., 2024) and Navix (Pignatelli et al., 2024) focus on gridworld navigation, Jumanji (Bonnet et al., 2023) spans domains from simple games to NP-hard combinatorial problems, Pgx (Koyamada et al., 2023) provides classic board games, and PuzzleJAX Earle et al. (2025) enables dynamic compilation of puzzle games.

Despite this coverage, a critical gap remains: classic arcade games. While MinAtar provides simplified versions of Atari games, the full visual complexity and authentic game mechanics of classic arcade games remain absent from the JAX ecosystem. Octax addresses this gap by providing the first end-to-end JAX implementation of classic arcade games through CHIP-8 emulation, delivering computational benefits while preserving the engaging gameplay mechanics that made arcade games valuable for algorithmic development.

## 3 Octax : The accelerated CHIP-8 Platform

Figure: Figure 2: Octax architecture: ROM loading, CHIP-8 emulation pipeline, and RL environment integration. The system transforms game ROMs through fetch-decode-execute cycles into vectorized JAX operations suitable for GPU acceleration.
Refer to caption: x1.png

This section presents our JAX implementation of CHIP-8 emulation. We detail the design decisions that enable GPU acceleration while maintaining behavioral fidelity to original games, and explain how CHIP-8’s architecture provides an optimal foundation for scalable experimentation in RL. Figure [2](https://arxiv.org/html/2510.01764v2#S3.F2) summarizes this section.

### 3.1 Why CHIP-8 for RL research?

CHIP-8 represents a strategic choice for RL environment design. Created in the 1970s as a virtual machine specification, CHIP-8 features a 64×32 monochrome display, 16 registers, 4KB memory, and 35-instruction set. These constraints, originally imposed by early microcomputer limitations, create several research advantages.

The platform provides image-based environments comparable to Atari games while offering some computational advantages. The 4KB memory footprint allows thousands of simultaneous game instances without memory constraints. The simple instruction set reduces emulation overhead compared to complex modern processors. The deterministic execution model ensures experimental reproducibility across different hardware configurations.

The platform supports everything from precise action games requiring split-second timing to complex puzzles demanding long-horizon planning. The 16-key input system provides sufficient complexity for interesting control challenges while remaining tractable for systematic analysis. Most importantly, CHIP-8 games are inherently modifiable and analyzable: their simple assembly code can be automatically generated, modified, and assessed for difficulty, enabling novel research directions in environment design and curriculum learning. This combination of Atari-like visual complexity with modern computational efficiency makes CHIP-8 well-suited for the JAX ecosystem, where extensive parallelization can transform week-long experiments into hour-long runs.

### 3.2 How does Octax work?

Octax converts CHIP-8 ROMs(^2^22ROM stands for Read-Only Memory, a type of storage originally used in game cartridges to hold software that cannot be modified by the user.) into vectorized RL environments while maintaining compatibility with original games. The implementation leverages JAX’s functional programming model and vectorization capabilities to enable GPU acceleration.

ROM loading and initialization. Game data is loaded from .ch8 files into the emulator’s 4KB memory space starting at address 0x200, following the standard CHIP-8 program layout first introduced in Weisbecker (1978). The system initializes with font data at address 0x50, sixteen general-purpose registers (V0-VF), an index register (I), a program counter (PC), and the 64×32 monochrome display buffer.

Fetch-decode-execute cycle. The core emulation loop implements the classic processor cycle using JAX primitives. The fetch() function retrieves 16-bit instructions from memory and advances the program counter. The decode() function extracts instruction components through bitwise operations, identifying opcodes, register indices, and immediate values. The execute() function uses JAX’s lax.switch for GPU-compatible instruction dispatch to specialized handlers.

Vectorized instruction execution. Instruction handlers follow JAX’s functional programming model, treating state as immutable and returning updated copies. ALU operations handle arithmetic and bitwise logic with carry/borrow flag management. Control flow instructions implement jumps, calls, and conditional operations using lax.cond. The display system uses vectorized operations to render sprites across the entire framebuffer simultaneously.

Environment integration. The OctaxEnv wrapper transforms the emulator into a standard RL interface. Each RL step executes multiple CHIP-8 instructions to maintain authentic game timing relative to the original 700Hz instruction frequency. The default frame skip setting preserves realistic game dynamics. Observations consist of the 64×32 display with 4-frame stacking, producing (4, 64, 32) boolean arrays. Actions map from discrete RL outputs to game-specific key subsets plus a no-op option. The wrapper manages delay and sound timers at 60Hz and executes startup sequences to bypass menu screens.

### 3.3 How to transform games into RL environments?

Converting CHIP-8 games into RL environments requires extracting reward signals and termination conditions from game-specific memory layouts and register usage patterns.

Score function design. Games store scores in different registers using various encoding schemes. Octax provides game-specific score_fn functions that extract scores from appropriate memory locations. Brix stores its score in register V5, incrementing with each destroyed brick. Pong encodes scores in BCD format within register V14, requiring score = (V[14] // 10) - (V[14] % 10) to compute player advantage. This flexibility allows researchers to experiment with alternative reward formulations based on different game state components.

Termination logic. Games signal completion through different register states that must be identified through analysis. Brix terminates when lives (V14) reach zero, while Tetris uses a dedicated game-state register (V1) that equals 2 on game over. Some games require compound conditions: Space Flight ends when either lives reach zero or a level completion counter exceeds a threshold, implemented as terminated = (V[9] == 0) | (V[12] >= 0x3E).

Action space optimization. Most games use subsets of the 16-key hexadecimal keypad. Octax supports custom action_set arrays that map RL action indices to relevant keys. Pong requires only keys 1 and 4 for paddle movement, while Worm uses directional keys 2, 4, 6, 8. This reduces action space size and accelerates learning by eliminating irrelevant inputs.

Initialization handling. Many games include menu screens that interfere with RL training. Octax supports startup_instructions parameters that automatically execute instruction sequences during environment reset, bypassing menus to begin gameplay immediately.

We address CHIP-8’s non-standardized scoring and termination by combining static ROM analysis and dynamic memory monitoring during gameplay, as detailed in Appendix [B](https://arxiv.org/html/2510.01764v2#A2).

### 3.4 Which games does Octax support?

Octax provides a curated collection of classic CHIP-8 games across multiple genres and difficulty levels. The current implementation includes 21 titles, with additional games planned for future releases. All environments maintain full compatibility with both Gymnasium and Gymnax APIs.

**Table 1: Currently implemented games in Octax.**
| Category | Available Games | Required Capabilities |
| --- | --- | --- |
| Puzzle | Tetris, Blinky, Worm | Long-horizon planning, spatial reasoning |
| Action | Brix, Pong, Squash, Vertical Brix, Wipe Off, Filter | Timing, prediction, reactive control |
| Strategy | Missile Command, Rocket, Submarine, Tank Battle, UFO | Resource management, tactical decisions |
| Exploration | Cavern (7 levels), Flight Runner, Space Flight (10 levels), Spacejam! | Spatial exploration, continuous navigation |
| Shooter | Airplane, Deep8, Shooting Stars | Simple reaction, basic timing |

The games (Figure [1](https://arxiv.org/html/2510.01764v2#S1.F1)) vary across multiple dimensions of difficulty and cognitive demand. Temporal complexity ranges from immediate reactions to long-term planning requirements. Spatial complexity spans single-screen environments to multi-screen worlds requiring navigation. Reward structures include both dense scoring mechanisms and sparse achievement-based systems. This systematic variation enables controlled studies of algorithmic performance across different challenge types while maintaining a unified technical framework for fair comparison. A categorization of these games is provided in Table [1](https://arxiv.org/html/2510.01764v2#S3.T1), with more detailed descriptions available in Appendix [B.2](https://arxiv.org/html/2510.01764v2#A2.SS2).

## 4 Experimental Evaluation

We evaluate Octax through RL training experiments across 16 diverse CHIP-8 games. Our goal is to demonstrate that the environments exhibit varied difficulties and learning dynamics suitable for RL research and benchmark the platform’s computational performance.

### 4.1 How do RL agents learn in Octax ?

Figure: Figure 3: PPO learning curves across 16 games: Interquartile Mean (IQM) returns using 10th-90th percentile ranges over 5M timesteps, with confidence intervals computed across 12 random seeds.
Refer to caption: x2.png

We train Proximal Policy Optimization (PPO) (Schulman et al., 2017) agents across our game suite due to its widespread adoption and proven scalability with parallel environments (Rudin et al., 2022).

##### Network architecture.

Our PPO agent(^3^33Based on Rejax implementation (Liesen et al., 2024).) uses a convolutional neural network designed for Octax’s (4, 64, 32) stacked observations. The feature extractor consists of three convolutional layers with 32, 64, and 64 filters respectively. These layers use kernel sizes of (8,4), 4, and 3 with corresponding strides of (4,2), 2, and 1. Extracted features are flattened and fed to separate actor and critic heads, each containing a single 256-unit hidden layer with ReLU activation throughout.

##### Training configuration.

We combine grid search optimization (detailed in Appendix [4](https://arxiv.org/html/2510.01764v2#S4)) on Pong with CleanRL’s standard Atari PPO hyperparameters (Huang et al., 2022). This yields GAE lambda of 0.95, clipping epsilon of 0.2, value function coefficient of 0.5, and entropy coefficient of 0.01. Each experiment uses 512 parallel environments with 32-step rollouts, 4 training epochs per update, and 32 minibatches for gradient computation. We apply the Adam optimizer (Kingma & Ba, 2014) with learning rate $5\times 10^{-4}$ and gradient clipping for stable training across 5 million timesteps per environment.

##### Experimental setup.

We conduct 12 independent training runs per game using different random seeds. All experiments run on a single NVIDIA A100 GPU with 24 concurrent training sessions. Agent performance is assessed every 131,072 timesteps on 128 parallel environments.

##### Results analysis.

The training curves in Figure [3](https://arxiv.org/html/2510.01764v2#S4.F3) reveal distinct learning profiles across games. We observe three main patterns that reflect different cognitive demands. *Rapid plateau games* (Airplane, Brix, Deep, Filter, Blinky) show quick initial learning followed by stable performance, suggesting clear reward signals. *Gradual improvement games* (Submarine, Tank, UFO) exhibit sustained learning throughout training, indicating either sparser reward structures or more complex strategic requirements. *Limited performance games*, like Tetris, exhibit significant variance with little absolute progress, making them difficult for standard policy gradient methods. Similarly, in Worm (a Snake clone), agents often manage to eat only a single apple before dying.

These learning profiles support the cognitive diversity of CHIP-8 environments, demonstrating that different games test varied aspects of learning and control. Individual training runs averaged 65 minutes each, with 24 experiments running concurrently, achieving approximately 30,800 environment steps per second across all parallel sessions.

### 4.2 How does Octax scale with parallelization?

Figure: Figure 4: Performance scaling of Octax and EnvPool across parallelization levels. The solid purple line is the number of steps per second (higher is better), and the dashed green line is the total execution time in seconds (lower is better).
Refer to caption: x3.png

##### Experimental setup.

We measure environment throughput across different parallelization levels to quantify Octax’s computational advantages. This experiment isolates pure computational benefits by fixing the game (Pong) and agent behavior (constant action) while varying parallel environment instances. Since all environments execute identical CHIP-8 computational cycles, these performance measurements apply uniformly across the entire game suite. To better interpret our results, we compare against EnvPool because it is widely adopted in RL research, using ALE Pong to assess CPU vs. GPU-based environment scalability.

##### Configuration.

We benchmark on a consumer-grade workstation with an RTX 3090 (24GB VRAM), 32GB RAM, and an Intel i7 processor (20 cores). We measure execution time for 100-step rollouts across varying parallel environment counts, with 50 independent measurements per configuration. The primary metric is environment steps per second, calculated as (number of environments × 100 steps) divided by execution time, where each step represents 4 frames due to Octax’s default frame skip setting.

##### Performance results.

Figure [4](https://arxiv.org/html/2510.01764v2#S4.F4) demonstrates near-linear scaling up to 350,000 steps (or 1,4M frames) per second with 8,192 parallel environments before hitting VRAM limitations. EnvPool running ALE Pong with all available CPU cores shows reduced scaling, plateauing around 25,000 steps per second due to CPU saturation. Octax achieves a 14× improvement in computational efficiency at high parallelization levels, reducing the computational cost of large-scale RL experiments. We also measured GPU memory usage across different environment counts, finding that execution memory scales linearly with the number of parallel environments with our benchmark script, consuming approximately 2 MB of GPU memory per environment

### 4.3 How do LLMs assist environment creation?

Figure: Figure 5: Rendering of the Target Shooter game, generated by an LLM, showing the player (left, circular object) and target (right, cross-shaped object).
Refer to caption: figures/target_shooter.png

Large language models (LLMs) have demonstrated a strong capability in code generation across diverse programming languages, enabling the automated creation of environments in RL research. Here we explore Octax’s capacity to accelerate research by leveraging LLMs to generate novel tasks, extending beyond manually designed game suites toward automated environment synthesis, as explored in Faldor et al. (2024).

##### Context.

During Octax’s development, we encountered several games where reward and termination logic proved difficult to extract through manual analysis of game mechanics. In these cases, we decompiled ROMs to obtain CHIP-8 assembly code and successfully employed LLMs to generate appropriate score_fn and terminated_fn functions by analyzing the assembly instructions. This process revealed LLMs’ capability to understand low-level game logic and translate it into RL-compatible reward structures. This success motivated us to investigate the reverse pipeline: using LLMs to generate complete CHIP-8 games from high-level descriptions, then leveraging Octax’s scalable simulation to evaluate these procedurally created environments.

Figure: Figure 7: Environment generation pipeline.
Refer to caption: figures/mermaid-diagram-2025-09-25-052406.png

Automated environment generation pipeline. Our pipeline consists of seven replicable steps for automatic CHIP-8 game generation. In Step 1, we construct a corpus of CHIP-8 tutorials, documentation, and programming examples, ensuring the LLM understands the architecture’s instruction set, memory layout, and common coding patterns. In Step 2, we embed this corpus into a prompt (detailed in Appendix [D.1](https://arxiv.org/html/2510.01764v2#A4.SS1)) that guides the LLM to produce syntactically correct CHIP-8 programs from high-level instructions. In Step 3, we provide a description of the game with desired mechanics, objectives, and constraints. In Step 4, the LLM generates the initial CHIP-8 code based on the provided description. In Step 5, an automated feedback loop between the LLM and a CHIP-8 compiler iteratively refines the code based on compilation errors until successful. In Step 6, Python wrapper functions for score_fn and terminated_fn are automatically generated, translating CHIP-8 registers into RL-compatible reward and termination signals. Finally, in Step 7, the game description is augmented to increase difficulty or introduce new challenges. Both the new description and the previously generated game are added to the LLM’s context before next iteration. Figure [7](https://arxiv.org/html/2510.01764v2#S4.F7) summarizes the automated environment generation pipeline.

Target Shooter case study. We validated this pipeline using Claude Opus 4.1, known for its proficiency in programming, with the following description: "Target Shooter – Targets appear randomly on the screen, and the player moves a crosshair to shoot them. Score increases per hit, and the game ends after a fixed number of targets." The system successfully generated three progressive difficulty levels: static targets for basic aiming skills, time-limited targets introducing decision pressure, and moving targets with time constraints requiring predictive aiming. Each level maintains consistent register mappings for score and termination, simplifying Octax compatibility. Figure [6](https://arxiv.org/html/2510.01764v2#S4.F6) shows how the LLM-generated environment visual appearance. All the code generated by the LLM is given in Appendix [D.2](https://arxiv.org/html/2510.01764v2#A4.SS2),

RL experiments. Using identical PPO configurations from Section [4.1](https://arxiv.org/html/2510.01764v2#S4.SS1), we trained agents on the three generated difficulty levels over 5M timesteps. Figure [6](https://arxiv.org/html/2510.01764v2#S4.F6) demonstrates clear performance stratification across difficulty levels: Level 1 agents achieved optimal returns of 10.0 with rapid convergence by 1M timesteps, Level 2 agents plateaued at 9.0 returns with moderate learning speed, while Level 3 agents reached 8.0 returns with the slowest progression. The inverse relationship between difficulty level and both final performance and sample efficiency indicates that our LLM-generated environments successfully create a meaningful difficulty gradient. This proof-of-concept demonstrates the feasibility of automated environment generation for RL research via Octax, with promising applications in curriculum learning, open-endedness, and continual learning scenarios.

## 5 Conclusion

We introduced Octax, a JAX-based CHIP-8 emulation platform that provides GPU-accelerated arcade game environments for reinforcement learning research. Our implementation achieves significant performance improvements over CPU-based alternatives, enabling experiments with thousands of parallel environments while maintaining perfect behavioral fidelity to original games. Through PPO evaluation across 16 diverse games, we demonstrated varied learning dynamics that highlight the cognitive diversity within classic arcade environments. The platform’s modular design enables both manual game integration and automated environment generation using large language models, providing researchers with flexible experimental design options.

Societal and environmental impact.
Octax enables more rigorous evaluation with larger sample sizes, addressing reproducibility concerns that affect institutions with limited computational resources. This implementation can reduce energy consumption compared to resource-intensive benchmarks such as ALE: experiments that once required top-tier clusters can now run efficiently on a single GPU, potentially saving significant compute time and resources.

Limitations.
The GPU-based architecture faces performance constraints due to CHIP-8’s variable instruction execution complexity. JAX synchronization across parallel environments means each step’s execution time depends on the slowest instruction among CHIP-8’s 35 operations, typically display rendering or complex ALU operations. The absence of established maximum scores across our game suite prevents the assessment of whether agents approach theoretical performance limits, limiting evaluation of algorithmic performance ceilings.

Future work.
Octax can expand through community contributions, with hundreds of compatible ROMs available online. The LLM-assisted environment generation pipeline enables curriculum learning and open-ended research through procedurally generated games that provide task diversity. We plan to investigate emulator optimizations including instruction-level parallelization strategies and adaptive batching to address synchronization bottlenecks from variable execution times. We also aim to extend platform support to Super-CHIP8 and XO-CHIP variants: Super-CHIP8 offers higher resolution displays (128×64) and extended instruction sets originally developed for HP48 calculators, while XO-CHIP provides color graphics, improved audio, and expanded memory while maintaining backward compatibility. These extensions would enable Octax to support more sophisticated games and visual complexity while preserving the computational efficiency advantages of the JAX-native architecture. Many CHIP-8 games feature multi-agent or multi-player mechanics, which we plan to support in future platform releases. The platform’s high-throughput capabilities also position it well for offline RL research, enabling the efficient creation of large-scale datasets and the comprehensive evaluation of offline algorithms across diverse game environments.

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## Appendix A CHIP-8 Technical Specifications

### A.1 Platform Overview

CHIP-8 was created by Joseph Weisbecker at RCA in the mid-1970s as a virtual machine for early microcomputers. The platform established one of the first successful portable gaming ecosystems by providing a hardware abstraction layer that enabled games to run across different systems.

### A.2 System Architecture

The CHIP-8 architecture consists of:

- •
Memory: 4KB total, with programs loaded at address 0x200
- •
Registers: 16 8-bit registers (V0-VF), with VF serving as a flag register
- •
Display: 64×32 pixel monochrome screen with XOR-based rendering
- •
Input: 16-key hexadecimal keypad (0-9, A-F)
- •
Timers: 60Hz delay timer and sound timer
- •
Audio: Single-tone buzzer

### A.3 Instruction Set Highlights

CHIP-8’s 35-instruction set includes specialized gaming primitives:

- •
Sprite Drawing (DXYN): XOR-based rendering enabling collision detection
- •
Key Input (EX9E, EXA1): Skip instructions based on key state
- •
BCD Conversion (FX33): Convert register values to decimal display
- •
Memory Operations: Bulk register loading/storing (FX55, FX65)

The XOR-based sprite system is particularly elegant: drawing the same sprite twice erases it, enabling simple animation and automatic collision detection when pixels turn off.

### A.4 Font System

CHIP-8 includes built-in 4×5 pixel font data for hexadecimal digits (0-F), stored at addresses 0x050-0x09F. Games reference these fonts for score and text display by setting the index register to the appropriate font location.

## Appendix B Game Environment Implementation Details

### B.1 Score Detection Methodology

CHIP-8 games store scoring information in arbitrary memory locations using game-specific formats. Our automated detection operates in two phases:

Static Analysis: We analyze ROM structure for common programming patterns, particularly binary-coded decimal (BCD) operations (FX33 instruction) that suggest numeric display routines.

Dynamic Monitoring: During human gameplay sessions, we monitor memory changes to correlate locations with scoring events. Register trend analysis identifies increasing values (likely scores) versus decreasing values (likely lives/health).

### B.2 Game List

#### B.2.1 Long-horizon Planning & Spatial Reasoning

Requires strategic thinking, spatial awareness, and multi-step planning

- •
tetris – Tetris by Fran Dachille (1991): Classic Tetris with piece rotation, movement, and dropping
- •
blinky – Blinky by Hans Christian Egeberg (1991): Pac-Man clone with 2 lives, maze with energy pills and 2 ghosts
- •
worm – SuperWorm V4 by RB-Revival Studios (2007): Snake-like game with enhanced controls and speed fixes

#### B.2.2 Timing, Prediction & Reactive Control

Requires precise timing, trajectory prediction, and fast reactive responses

- •
brix – Brix by Andreas Gustafsson (1990): Breakout clone with paddle controlling the ball to destroy bricks, 5 lives
- •
pong – Pong: Single player pong game with paddle control
- •
squash – Squash by David Winter (1997): Bounce ball around squash court with paddle
- •
vertical_brix – Vertical Brix by Paul Robson (1996): Breakout variant with vertical brick layout and paddle movement
- •
wipe_off – Wipe Off by Joseph Weisbecker: Move paddle to wipe out spots, 1 point per spot, 20 balls
- •
filter – Filter: Catch drops from pipe with paddle

#### B.2.3 Resource Management & Tactical Decisions

Requires managing limited resources and making strategic tactical choices

- •
missile – Missile Command by David Winter (1996): Shoot 8 targets using key 8, earn 5 points per target, 12 missiles total
- •
rocket – Rocket by Joseph Weisbecker (1978): Launch rockets to hit moving UFO across top of screen, 9 rockets total
- •
submarine – Submarine by Carmelo Cortez (1978): Fire depth charges at submarines, 15 points for small, 5 for large subs
- •
tank – Tank Battle: Tank with 25 bombs to hit mobile target, lose 5 bombs if tank hits target
- •
ufo – UFO by Lutz V (1992): Stationary launcher shoots in 3 directions at flying objects, 15 missiles

#### B.2.4 Exploration & Continuous Navigation

Requires spatial exploration, obstacle avoidance, and continuous movement control

- •
cavern – Cavern by Matthew Mikolay (2014): Navigate cave without hitting walls, modified for leftward exploration
- •
flight_runner – Flight Runner by TodPunk (2014): Simple flight navigation game
- •
space_flight – Space Flight by Unknown (19xx): Fly through asteroid field using ship navigation controls
- •
spacejam – Spacejam! by William Donnelly (2015): Enhanced ship tunnel navigation game

#### B.2.5 Simple Reaction & Timing

Requires basic reaction time and simple decision making

- •
airplane – Airplane: Bombing game where you drop bombs by pressing key 8
- •
deep – Deep8 by John Earnest (2014): Move boat left/right, drop and detonate bombs to destroy incoming squid
- •
shooting_stars – Shooting Stars by Philip Baltzer (1978): Classic shooting game

## Appendix C Hyperparameter Optimization Results

We conducted a comprehensive grid search on the Pong environment to identify optimal PPO hyperparameters before evaluating across the full game suite. The search explored four key dimensions: number of parallel environments, rollout length, minibatch size, and learning rate. All experiments used 4 epochs per update, GAE lambda of 0.95, and gradient clipping at 0.5.

### C.1 Search Space

The hyperparameter search explored the following ranges:

- •
Environments: $\{128,256,512,1024\}$
- •
Rollout steps: $\{32,64,128,512\}$
- •
Minibatches: $\{4,8,16,32\}$
- •
Learning rate: $\{2.5\times 10^{-4},5\times 10^{-4},1\times 10^{-3}\}$

Each configuration was trained for 1M timesteps with evaluation every 65,536 steps. Final evaluation scores represent the last recorded performance, where less negative values indicate better performance.

### C.2 Results Summary

Table [2](https://arxiv.org/html/2510.01764v2#A3.T2) presents the key configurations and their final evaluation scores. Higher scores indicate better performance (scores are negative, with values closer to zero being better).

**Table 2: Hyperparameter search results on Pong environment. Configurations sorted by final evaluation score.**
| Envs | Steps | Minibatches | LR | Score |
| --- | --- | --- | --- | --- |
| 512 | 32 | 32 | 0.0005 | -2.34 |
| 512 | 32 | 16 | 0.001 | -2.48 |
| 512 | 32 | 32 | 0.001 | -2.69 |
| 128 | 128 | 16 | 0.00025 | -2.95 |
| 128 | 64 | 8 | 0.00025 | -3.19 |
| 512 | 32 | 16 | 0.00025 | -3.20 |
| 256 | 64 | 16 | 0.00025 | -3.38 |
| 128 | 32 | 4 | 0.00025 | -3.44 |
| 128 | 64 | 16 | 0.00025 | -3.53 |
| 512 | 32 | 16 | 0.0005 | -3.73 |
| 256 | 32 | 4 | 0.00025 | -3.78 |
| 128 | 128 | 8 | 0.00025 | -3.91 |
| 256 | 128 | 32 | 0.00025 | -4.03 |
| 512 | 64 | 16 | 0.00025 | -4.17 |
| 1024 | 32 | 32 | 0.00025 | -4.34 |
| 1024 | 32 | 16 | 0.00025 | -4.44 |
| 256 | 128 | 16 | 0.00025 | -4.66 |
| 1024 | 64 | 32 | 0.00025 | -4.96 |

### C.3 Analysis and Key Findings

Learning rate impact. Higher learning rates significantly improved performance, with $5\times 10^{-4}$ and $1\times 10^{-3}$ substantially outperforming $2.5\times 10^{-4}$. The top three configurations all used learning rates above the commonly used $2.5\times 10^{-4}$.

Environment scaling. 512 parallel environments provided the optimal balance between computational efficiency and sample diversity. Configurations with 1024 environments showed diminishing returns, possibly due to computational overhead or reduced gradient update frequency.

Rollout length. Shorter rollouts (32 steps) consistently outperformed longer ones, indicating more frequent policy updates may be beneficial for this environment.

Minibatch size. Larger minibatch sizes (16-32) generally improved performance by providing more stable gradient estimates, though the effect was less pronounced than learning rate changes.

### C.4 Final Configuration

Based on these results, we selected the following hyperparameters for all subsequent experiments:

- •
Parallel environments: 512
- •
Rollout steps: 32
- •
Training epochs: 4
- •
Minibatches: 32
- •
Learning rate: $5\times 10^{-4}$
- •
GAE lambda: 0.95
- •
Clip epsilon: 0.2
- •
Value function coefficient: 0.5
- •
Entropy coefficient: 0.01

## Appendix D LLM-Assisted Environment Generation

This appendix details the automated environment generation pipeline using large language models (LLMs) to create novel CHIP-8 games for reinforcement learning research. We demonstrate the complete process from prompt engineering to code generation across three difficulty levels of a Target Shooter game.

### D.1 Prompt Engineering

Our LLM generation pipeline relies on carefully crafted prompts that provide comprehensive CHIP-8 programming context and specific game requirements. The core prompt structure includes CHIP-8 architectural constraints, Octo assembly language syntax, and reinforcement learning compatibility requirements.

Figure: Listing 1: LLM prompt template for CHIP-8 game generation

The prompt incorporates several key components:

- •
Role specification: Establishes the LLM as a professional CHIP-8 developer
- •
Technical constraints: Emphasizes syntactic correctness and CHIP-8 compliance
- •
RL compatibility: Specifies requirements for score tracking and termination detection
- •
Reference material: Includes comprehensive CHIP-8 documentation and examples
- •
Game description: Placeholder for specific game mechanics and objectives

The prompt template includes placeholder tags that are populated with comprehensive CHIP-8 resources: <documentation> contains the official Octo Manual ([https://johnearnest.github.io/Octo/docs/Manual.html](https://johnearnest.github.io/Octo/docs/Manual.html)), <tutorial1> includes the Beginner’s Guide ([https://johnearnest.github.io/Octo/docs/BeginnersGuide.html](https://johnearnest.github.io/Octo/docs/BeginnersGuide.html)), <tutorial2> incorporates the Intermediate Guide ([https://johnearnest.github.io/Octo/docs/IntermediateGuide.html](https://johnearnest.github.io/Octo/docs/IntermediateGuide.html)), and <example> provides a complete game implementation ([https://github.com/JohnEarnest/chip8Archive/blob/master/src/outlaw/outlaw.8o](https://github.com/JohnEarnest/chip8Archive/blob/master/src/outlaw/outlaw.8o)) to demonstrate best practices and coding patterns.

### D.2 Generated Target Shooter Implementation

Using the prompt template, we generated three progressive difficulty levels of a Target Shooter game. Each level maintains consistent register mappings for score and termination while introducing increasing complexity in target behavior and timing constraints.

#### D.2.1 Level 1: Static Targets

The first difficulty level features stationary targets that appear at random locations, focusing on basic aiming and shooting mechanics.

Figure: Listing 2: Level 1 Target Shooter - Static targets

#### D.2.2 Level 2: Time-Limited Targets

The second level introduces time pressure by making targets disappear after a fixed duration, requiring faster decision-making from RL agents.

Figure: Listing 3: Level 2 Target Shooter - Time-limited targets

#### D.2.3 Level 3: Moving Targets with Time Constraints

The most challenging level combines target movement with time limits, requiring predictive aiming and rapid response times.

Figure: Listing 4: Level 3 Target Shooter - Moving targets with time constraints

#### D.2.4 Environment Integration and Wrapper Implementation

Once the LLM generates the CHIP-8 assembly code for each difficulty level, the games require integration with Octax’s reinforcement learning interface. The environment wrapper extracts reward signals and termination conditions from the consistent register mapping established during code generation.

The Target Shooter implementation demonstrates the integration between LLM-generated content and the Octax framework. Each level maintains identical register assignments to ensure compatibility across the difficulty progression, enabling curriculum learning experiments without code modifications.

Figure: Listing 5: Target Shooter environment wrapper implementation

The consistent register mapping across all three levels enables direct comparison of agent performance and facilitates automated curriculum progression. Register V[2] consistently stores the score for reward calculation, while V[3] serves as the binary termination flag. The five-action control scheme (WASD movement plus shoot) provides sufficient complexity for interesting policies while remaining tractable for systematic analysis.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: - How many attempts were required to generate Target Shooter using Claude? Was the success rate?
soundness: 3
strengths: - The performance improvement of Octax over vectorized environments is impressive and will decrease the training time for many researchers using JAX to engage in RL experiments. With a shorter training time on simpler games, many new experiments to understand the training dynamics and tradeoffs between the large number of RL hyperparameters may be investigated more effectively.
- On top of improved acceleration, the authors demonstrate the ability of LLMs to aid in the understanding of existing ROMs and the generation of new environments based on properly constructed prompts. I believe this may be a simplified and interesting sandbox to explore LLM coding in a way that is faster than traditional code generation procedures.
- Providing this code as a testbed will facilitate the construction of more complex environments, such as Super-CHIP8, to add further complexity and interest.
summary: In this paper, the authors present a JAX implementation, known as OCTAX, of the CHIP-8 platform amenable to GPU acceleration. Octax converts CHIP-8 ROMs into RL environments that can take advantage of vectorized JAX functions. Octax provides a wide selection of games of interest to RL researchers spanning a number of different genres and difficulty levels. The performance of Octax is shown to be substantially higher than traditional methods for environment simulation using parallel instances of games with EnvPool. The authors note that some CHIP-8 ROMs had difficult reward functions to implement and understand, but this process was made easier with the help of LLMs to study the machine code. By reversing this process, they were able to train LLMs to produce new games that could be added to the RL training environment.
weaknesses: - The utility of arcade environments for SOTA RL has passed, so the real value is to use the environment to accelerate understanding of RL training dynamics and other metrics. Although the added challenge of generating new environments may mitigate this point.
- Supporting yet another training environment may have a limited impact and lower the contributions.
- There's no discussion regarding the success rate of generating ROMs using LLMs. I assume this is for the sake of space and to focus the discussion of the paper on the acceleration of arcade environments.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Re Emulator fidelity: Do you have systematic validation tests? E.g. for something like Pong, can you provide instruction-by-instruction equivalence tests, frame-by-frame rendering comparisons, and unit tests across representative ROMs against a trusted CHIP-8 interpreter.

Baselines and fairness: If you retain EnvPool/ALE comparisons, justify differences in environment complexity or compare EnvPool on a matched low-resolution workload. Additionally, consider adding DQN-style results.

Profiling and memory breakdown: Provide GPU profiler traces (kernel times, GPU utilization) and a detailed memory breakdown per environment (state arrays, framebuffers, intermediate buffers, compiled executable memory). Explain how you measured ~2 MB/env and the causes of scaling limits.

PPO protocol: Were hyperparameters tuned per game or held fixed? Provide ablations showing sensitivity to frame-skip and observation stacking and report whether reported learning curves use per-game tuning.

LLM pipeline evaluation: What fraction of LLM-generated ROMs compiled and ran without manual edits? How often did generated score_fn/terminated_fn require human correction? Provide statistics and typical failure cases and describe any automated validation used.
soundness: 3
strengths: The paper addresses a clear practical gap in the JAX ecosystem: image-based, GPU-native environments for RL. It thereby comes with significant engineering effort: a fully vectorized CHIP-8 emulator in JAX that can execute many parallel instances and integrates with standard RL training loops (Gym/Gymnax). Furthermore, this work directly empowers the community to work on more complex JAX-accelerated environments, which wasn't directly possible beyond Craftax before.

It introduces a broad catalog of 21 CHIP-8 games across multiple genres, useful for rapid prototyping and curriculum experiments. The reported throughput and scaling is promising, and if validated could substantially reduce wall-clock time for many experiments allowing for low resource algorithmic developments.

Finally, the authors for provide evidence for a novel auxiliary idea: an LLM-assisted pipeline to generate lightweight CHIP-8 games and wrappers, enabling rapid environment prototyping. I find this especially exciting since it may enable open-ended training and novel autocurricula approaches.
summary: The paper presents OCTAX, a JAX-native, vectorized CHIP-8 emulator and RL environment suite that runs many GPU-accelerated game instances. OCTAX exposes (21) CHIP-8 titles as Gym/Gymnax-compatible environments, includes wrappers to extract score and termination signals, reports high throughput (claims up to hundreds of thousands env steps/s / millions of frames/s), provides PPO training experiments across multiple games, and demonstrates an LLM-assisted pipeline to generate CHIP-8 games and corresponding reward/termination wrappers. An anonymized code repository is provided.
weaknesses: Emulation fidelity claims ('perfect fidelity') may be partially unsubstantiated: no instruction-trace equivalence, frame-by-frame comparison, or unit tests against a trusted CHIP-8 interpreter are reported. Most of the functional correctness assertions come from the successful training of agents.

Arguably, there is no really fair CPU baseline for CHIP-8 measured on the same machine, and comparisons to EnvPool/ALE conflate environment complexity differences. I understand that this is not trivial to accomplish, but I think it would make sense to maybe show runtimes of CPU/GPU-enabled environments just for contextualization. Even if they are not the same as the ones implemented in OCTAX.

The GPU memory/accounting claims (~2 MB per environment) lack a principled breakdown and appear implausible without explanation of XLA/JAX buffer and compiled executable overheads.

The LLM-assisted game generation is presented as a single case study with no statistics on compile/run success rates, human edit frequency, or failure modes.

Some broader claims (energy savings, enabling small labs) are asserted without corresponding measured energy or cost data. This could and should be better substantiated.

All experiments only consider PPO as the single RL algorithm tested.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 3
questions: - Is this benchmark only usable to those who have algorithms written in JAX?
- Does this benchmark have a set of rigorous set of specifications (number of frames, number of games, way to compare different overall performance) to ensure that researchers can easily compare their work?
- How challenging are the existing environments in this benchmark? Can you benchmark some different algorithms and provide code for these in the repository?
- Do all environments have a source of stochasticity? Or can they be solved by brute-force style algorithms?

Also, it appears Line 79 has a mistake. While I'm strongly in favor of benchmarks which make research easier, I feel this work still has a long way to go before it could replace something like Atari, thus I cannot yet recommend acceptance.
soundness: 3
strengths: This paper aims to achieve the important goal of reducing the cost of running Reinforcement Learning experiments, clearly succeeding, achieving an impressive 1.4 million frames per second on consumer-grade hardware. The code already being open-source is also a nice positive. LLM-generated environments using the provided prompts provide a nice way for developers to test environments with specific properties if they wish.
summary: This paper presents Octax, a high-performance arcade-style benchmark for Reinforcement Learning in Jax. The paper utilizes end-to-end GPU training, allowing extremely high throughput. Primarily, this work looks to provide a viable alternative to the long-standing Atari benchmark, but using considerably less computational resources. Furthermore, the paper presents a way to quickly use LLMs to generate new environments, on top of the current set of games presented.
weaknesses: While I appreciate the attempt to reduce the computational burden of the Atari, I feel that the authors missed many key reasons why the Atari benchmark became so popular, and many of the features that still make it so popular today. In its current form, I don’t believe this work is capable of replacing the Atari benchmark without major reforms for a few key reasons:

- **A clear, fixed way to evaluate** - One reason Atari is useful is that there is a relatively clear and fixed protocol for evaluation [1]. This means researchers know what environments they need to run, the settings they need to use, and know that their work will be comparable to others. Furthermore, there are precise guidelines on how scores should be reported [2]. The paper would benefit from being very clear about what environments are included in the benchmark, how many frames/timesteps algorithms should be trained for, and how results should be reported, with examples. Currently Figure 3 does not meet the same standards as many papers which use the Atari benchmark.
- **Aggregate Scores** - Atari is useful as a benchmark, as the performance of an algorithm can be boiled down to a single number or single graph. In Atari, this has become human-normalized IQM performance with 95\% confidence intervals, which provides a score for the given algorithm. This prevents games with different score magnitudes from dominating the final score. Currently, this appears to be missing, despite games having scores of different magnitudes.
- **No-op Starts, Sticky Actions and Random Actions** - In the Atari benchmark, features are provided that prevent the agent from exploiting determinism (preventing brute-force approaches). Specifically, no-op starts randomly uses up to 30 no-op steps at the start of episodes; sticky actions give a 25% chance for actions to be repeated, and agents are forced to take random actions 1% of the time. While Octax appears to have some environments that have randomness, it's unclear whether brute-force approaches could work in some environments. 
- **JAX only** - While JAX has its advantages in speed, for a benchmark, I see it as a significant weakness if this benchmark is exclusive only to JAX users. A significant portion of RL researchers and users use other frameworks, such as PyTorch, which appear to be excluded from using this benchmark.
- **Historic data** - One major advantage of Atari is that a huge number of algorithms have been evaluated, making it useful to compare against new algorithms, while Octax only has PPO. Please consider adding more algorithms such as DQN [3], SAC [4], PQN [5], and also more state-of-the-art algorithms would be appreciated.
- **Game categorizations** - Currently, in Octax games are categorized into groups such as Puzzle, Action, etc. I think it would be more beneficial for the research community if environments were grouped by the aspects of the algorithm they challenge. In Atari, there are well known groups such as hard exploration (Montezuma’s Revenge), long term-credit assignment (Skiing) and many more. For a good example of this, please look at BSuite [6]. While Table 1 somewhat provides this, I still don’t think it is up to the standards of recent work. Furthermore, I’d appreciate a more detailed description of the tasks, including the frequency and magnitude of rewards. It is currently unclear if  Octax provides environments that are as challenging as Atari - for example, even after years of research, environments such as Pitfall and Montezuma’s Revenge are still extremely challenging.


[1] Machado, Marlos C., et al. "Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents." Journal of Artificial Intelligence Research 61 (2018): 523-562.

[2] Agarwal, Rishabh, et al. "Deep reinforcement learning at the edge of the statistical precipice." Advances in neural information processing systems 34 (2021): 29304-29320.

[3] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).

[4] Haarnoja, Tuomas, et al. "Soft actor-critic algorithms and applications." arXiv preprint arXiv:1812.05905 (2018).

[5] Gallici, Matteo, et al. "Simplifying Deep Temporal Difference Learning." The Thirteenth International Conference on Learning Representations.

[6] Osband, Ian, et al. "Behaviour Suite for Reinforcement Learning." International Conference on Learning Representations.

---

confidence: 5
contribution: 4
initial_rating: 8
presentation: 3
questions: **Generalization / external validity.**
Do you have any evidence that OCTAX performance correlates with Atari performance, Procgen performance, or other visual RL benchmarks? Even a tiny pilot (e.g. rank-correlation of seed-averaged scores across algorithms) would strengthen the “Atari alternative” positioning. 

**EnvPool/CuLE comparison.**
Can you report CuLE numbers (GPU Atari) on the same GPU you used for OCTAX, or at least discuss why that wasn’t feasible? That would make the “14× faster” claim feel less like apples vs oranges. Also, have you tried running OCTAX on CPU only to show the CPU→GPU delta cleanly? 

**Automatic reward/termination inference.**
For a new arbitrary CHIP-8 ROM (unseen by you), how robust is score/termination extraction? Do you have quantitative success rates for the static+dynamic heuristics or the LLM-generated wrappers? For RL practitioners, “plug in a ROM and it just works” is a killer feature—please convince us it's realistic.
soundness: 3
strengths: **S1. Clear problem, clear value to the community.**
The paper correctly identifies a real bottleneck in RL: environments are still mostly CPU-bound, even though policy learning is GPU-bound. This prevents large-scale sweeps, high seed counts, and rigorous statistics, especially for vision-based tasks like Atari. OCTAX directly targets that gap with an image-based, arcade-style benchmark that runs entirely in JAX on GPU. 

**S2. Strong engineering contribution.**
The authors implement a full CHIP-8 fetch/decode/execute loop in JAX using functional state passing, vectorized dispatch (lax.switch), and batched framebuffers, and wrap it as Gym/Gymnax-style RL envs with reward extraction, termination logic, action pruning, frame stacking, etc. They argue that fidelity to original game mechanics is preserved. 
This is nontrivial and, as far as I know, not previously available in the JAX ecosystem for Atari-like games.

**S3. Throughput + scaling results are compelling.**
Reported numbers (350k steps/sec, linear-ish scaling up to 8k envs on a single 3090; 14× over EnvPool Pong at high parallelism) are impressive and very relevant to labs that don't have multi-node clusters. The memory footprint (2 MB per env, linear in env count) is also concretely discussed. 
This is an unusually thorough systems evaluation for an RL benchmark paper.

**S4. Diversity of tasks and empirical characterization.**
They don’t just dump environments; they also measure PPO learning curves across 16 games and group them into qualitative regimes (fast plateau vs. gradual improvement vs. hard/sparse). This suggests these games could be used for algorithm diagnostics, curriculum, etc.
summary: This paper introduces OCTAX, a suite of classic arcade-style RL environments implemented as a fully vectorized CHIP-8 emulator in JAX. The goal is to provide an Atari-like benchmark that (a) runs end-to-end on GPU, (b) scales to thousands of parallel environments, and (c) maintains authentic game dynamics. The authors show throughput up to ~350k steps/sec (≈1.4M frames/sec) with 8,192 parallel environments on a single consumer GPU (RTX 3090), which they report is roughly 14× faster than CPU-bound baselines like EnvPool on Atari Pong. 

The benchmark covers 20+ CHIP-8 games across genres (puzzle, action, navigation, resource management, etc.), and the paper provides PPO training curves for 16 of them. These curves exhibit meaningful diversity in difficulty and learning dynamics (fast plateau vs. gradual improvement vs. failure-to-learn cases like Tetris/Worm), suggesting this is not just “16 clones of Breakout,” but a range of cognitive burdens. 

Finally, the paper explores automatic environment generation: they use an LLM to synthesize new CHIP-8 games (e.g. a “Target Shooter” with increasing difficulty levels), plus score/termination logic, and then successfully train PPO on these generated tasks with clean difficulty gradients. This is pitched as a path toward scalable curriculum / rapid task creation. 

Overall, the paper proposes OCTAX as “the missing GPU-native Atari for JAX RL,” arguing that this makes statistically reliable RL experiments cheaper and more reproducible for smaller labs.
weaknesses: **W1. How “Atari-like” is it, really? (External validity.)**
CHIP-8 games are dramatically simpler than Atari 2600 in terms of resolution (64×32 monochrome), action semantics, and world complexity. The paper asserts “Atari-like cognitive demands,” but the qualitative gap (e.g., long-horizon exploration, partial observability, rich object interactions) is not deeply quantified. We see PPO struggling on Tetris/Worm, but I'd like more systematic evidence that success on OCTAX predicts anything on Atari, NetHack, Procgen, etc. 
Right now OCTAX looks great for intra-JAX benchmarking and ablations, but it's less clear if it’s meaningful as a drop-in Atari replacement for algorithm claims.

**W2. Reward shaping / termination extraction feels artisanal.**
For each game, they manually or semi-automatically infer score registers, life counters, game-over flags, menu skips, etc. (e.g. “Brix stores score in V5, Pong encodes BCD in V14, etc.”). This is powerful, but also fragile and slightly underspecified. 
If a new contributor adds a weird ROM, how confident are we that OCTAX's automatic heuristics won't silently produce a broken reward function (e.g. rewarding losing health)? The paper mentions static+dynamic analysis plus some LLM help, but I’d like stronger guarantees or validation.

**W3. No baselines beyond PPO.**
All learning curves are PPO only. There's no DQN-style baseline, no lightweight world-model baseline, no offline RL baseline, etc. PPO is reasonable (and popular), but I'd like to know if these environments produce meaningful rankings across algorithms, or whether they’re PPO-biased (e.g. continuous control style tuning, frame stacking assumptions, frame-skip assumptions). 
Right now we mainly learn “PPO can learn some of them.”


Decision: Accept (Poster)