Add LanguageReward for training models to think in target language

This commit introduces a new reward function that encourages models to
think in a specific target language within their <think> tags.

Key changes:
- Add LanguageReward class to src/forge/data/rewards.py
  - Uses langid for language detection
  - Configurable target language (ISO 639-1 codes)
  - Returns full_reward for language match, no_match_reward otherwise
  - Raises helpful error if langid not installed

- Add comprehensive unit tests in tests/unit_tests/rl/test_language_reward.py
  - Tests for multiple languages (English, Japanese, Chinese, Spanish, etc.)
  - Tests for edge cases and error handling
  - All 28 tests passing

- Create sandbox/grpo_language/ app for experimentation
  - Extends apps/grpo/ with LanguageReward
  - Hardcoded to Japanese (ja) as default target language
  - Includes README with usage instructions
  - Config file for Qwen3-1.7B model

Implementation details:
- Extracts text from <think></think> tags for analysis
- Concatenates multiple thinking blocks for language detection
- Compatible with existing MathReward and ThinkingReward
- Does not add langid to requirements.txt (optional dependency)

Usage:
python -m sandbox.grpo_language.main --config sandbox/grpo_language/qwen3_1_7b.yaml

Note: Requires 'pip install langid' before use

Author: casteryh

sandbox/grpo_language/README.md

@@ -0,0 +1,81 @@
+# GRPO with Language Reward
+
+This sandbox app demonstrates using GRPO training with a language reward that encourages the model to think in a specific target language.
+
+## Overview
+
+This app extends the standard GRPO training (from `apps/grpo/`) by adding a `LanguageReward` that evaluates whether the model's thinking (text within `<think></think>` tags) is in the target language.
+
+## Key Features
+
+- **Multi-objective training**: Combines three rewards:
+  - `MathReward`: Evaluates correctness of math answers
+  - `ThinkingReward`: Encourages use of thinking tags
+  - `LanguageReward`: Rewards thinking in target language (Japanese by default)
+
+- **Language detection**: Uses `langid` to detect the language of thinking blocks
+
+- **Configurable target language**: While this app defaults to Japanese (`ja`), the `LanguageReward` can be configured for any ISO 639-1 language code
+
+## Requirements
+
+Before running this app, install the required language detection library:
+
+```bash
+pip install langid
+```
+
+## Usage
+
+```bash
+python -m sandbox.grpo_language.main --config sandbox/grpo_language/qwen3_1_7b.yaml
+```
+
+## How It Works
+
+1. The model receives a math problem and is instructed to use `<think>` tags for reasoning
+2. During training, the model generates responses with thinking blocks
+3. Three rewards are computed:
+   - Math correctness (did it get the right answer?)
+   - Thinking usage (did it use thinking tags properly?)
+   - Language usage (did it think in Japanese?)
+4. The model is trained to maximize all three rewards
+
+## Configuration
+
+The target language is hardcoded as Japanese in `main.py` (line 321):
+
+```python
+LanguageReward(target_language="ja")
+```
+
+To use a different language, modify this line with the appropriate ISO 639-1 code:
+- English: `"en"`
+- Chinese: `"zh"`
+- Spanish: `"es"`
+- French: `"fr"`
+- etc.
+
+## Expected Behavior
+
+Over the course of training, the model should learn to:
+1. Solve math problems correctly
+2. Use `<think></think>` tags for its reasoning
+3. Write its thinking in Japanese (or the configured target language)
+
+## Metrics
+
+The following metrics are logged to W&B:
+- `reward/evaluate_response/avg_LanguageReward_reward`: Average language reward
+- `reward/evaluate_response/avg_MathReward_reward`: Average math reward
+- `reward/evaluate_response/avg_ThinkingReward_reward`: Average thinking reward
+- `reward/evaluate_response/avg_total_reward`: Average of all rewards
+
+## Differences from Standard GRPO
+
+This is a modified version of `apps/grpo/main.py` with:
+1. Added import: `from forge.data.rewards import LanguageReward`
+2. Modified reward functions list to include `LanguageReward(target_language="ja")`
+3. Updated config to use different W&B group name
+
+All other training logic remains the same.