Complete, runnable reward functions for TRL's GRPOTrainer. Every function here follows the current TRL reward-function signature: it accepts completions plus any extra dataset columns as keyword arguments, and returns a list[float] the same length as completions. Base mod. Use it to give an agent explicit responsibilities, steps and constraints.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
FunDSP is an audio DSP (digital signal processing) library for audio processing and synthesis. Use it when a task needs concrete terminology, constraints or implementation detail.
Kaolin packages reusable building blocks from NVIDIA 3D research into a cohesive PyTorch API — continuously improving representation-agnostic physics simulation, fast conversions between representations, quaternion math, batched mesh and splat containers, I/O, visualization and m. Use it to build a structured path from fundamentals to hands-on practice.
It can be also used to convert your MQL4 code into MQL5 with minimum code changes. Projects implementing this framework: Multi-strategy advanced trading robot.
Complete, runnable reward functions for TRL's GRPOTrainer. Every function here follows the current TRL reward-function signature: it accepts completions plus any extra dataset columns as keyword arguments, and returns a list[float] the same length as completions. Base mod. Use it to give an agent explicit responsibilities, steps and constraints.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
FunDSP is an audio DSP (digital signal processing) library for audio processing and synthesis. Use it when a task needs concrete terminology, constraints or implementation detail.
Kaolin packages reusable building blocks from NVIDIA 3D research into a cohesive PyTorch API — continuously improving representation-agnostic physics simulation, fast conversions between representations, quaternion math, batched mesh and splat containers, I/O, visualization and m. Use it to build a structured path from fundamentals to hands-on practice.
It can be also used to convert your MQL4 code into MQL5 with minimum code changes. Projects implementing this framework: Multi-strategy advanced trading robot.